SMART PROTECTION SYSTEM FOR FUTURE …Smart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources i Keywords Adaptive Overcurrent

SMART PROTECTION SYSTEM FOR

FUTURE POWER SYSTEM DISTRIBUTION

NETWORKS WITH INCREASED

DISTRIBUTED ENERGY RESOURCES

Thesis

Moses Kavi

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Electrical and Computer Science

Faculty of Science and Engineering

Queensland University of Technology

2019

Smart Protection System for Future power System Distribution Networks with Increased Distributed Energy

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Keywords

Adaptive Overcurrent Protection

Adaptive Radial Distribution Feeder Protection

Arc-Fault Detection

DC Arc-Fault Detection

DC – Offset Suppression

Future Electricity Distribution Network

High impedance Fault Detection

High impedance Fault Feature Extraction

Intelligent Electronic Devices

Inverse-Time Overcurrent Relaying

Mathematical Morphology

Morphological Filter

Overcurrent Protection

Photovoltaic System Protection

Smart Power System Protection

Weighted Structuring Element

iiSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

Abstract

Existing distribution feeders and their integrated protection systems are not

designed for high penetration of renewable energy (RE) based distributed energy

resources (DERs). The overcurrent protection systems are designed considering the

passive, unidirectional current flow. However, integration of the RE based DERs

such as PV systems through power electronic inverter interfaces fundamentally

changes the distribution network from passive to active network with bidirectional

current flow. The increased use of inverter interfaced RE based DERs and loads will

result in increased harmonic injection affecting power quality. Moreover, increased

penetration of RE based DERs will reduce the level of fault current magnitude from

the feeder substation source. This will adversely affect the feeder protection system

to provide effective protection as the fault current could fall below the overcurrent

threshold.

Faults in power systems (both in AC and DC system) are inevitable and will

occur at one time or another. Certain fault types, such as high impedance faults (HIF)

in AC systems generate low fault current magnitude as opposed to high fault current

magnitude from common short circuit faults which renders the feeder overcurrent

(OC) protection mechanism ineffective in HIF detection. This type of faults must be

detected and isolated as they can cause fire hazards and increase the risk of

electrocution. The inherent difficulty in HIF detection using OC protection scheme in

medium- (MV) to low voltage (LV) where HIFs are a common occurrence can be

aggravated by penetration of RE based DERs. HIF detection and classification based

on feature extraction rather than simply using current magnitude as a metric for HIF

detection will fail. This is due to low fault current magnitude from HIFs and

moreover, increased penetration of RE based DERs that reduces the fault current

magnitude.

Short circuit faults on the other hand result in large fault current having

potential to cause severe damage to power system apparatus and switchgear as well

as causing instability to the unaffected portion of the power system, thus must be

speedily detected and isolated. Short-circuit fault conditions generate transients in

fault current with an exponentially decaying DC-offset. The DC-offset distorts the


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fault signal waveform and may compromise the integrity of the relay algorithms such

as those based on fast Fourier transform (FFT) and wavelet transform (WT) thereby

resulting in computational delays in the detection of the fault condition. As the

accuracy and speed of convergence of conventional FFT and WT relies on the

periodicity of the fault current and voltage, their effectiveness under DC-offset and

HIFs are limited. Moreover, most DC-offset suppression techniques utilise parameter

estimation, and can add additional computational delay.

Fault protection systems in DC distribution are at their infancy as compared to

the fault protection systems in AC distribution. Faults in DC systems including DC

side of PV system exhibit characteristics quite different from AC system generally

because of different voltage (V) and current (I) characteristics in DC systems. DC

systems generally suffer from short circuit as well as open circuit faults resulting

from mechanical separation of conductors, and in most cases resulting in sustained

arcing. An overcurrent protection strategy using current magnitude as a threshold

metric is applied for all types of faults in the DC power systems including PV

systems. However, not all fault conditions on the DC system can be adequately

protected using such a strategy. One such fault condition is the DC arc-fault

occurring on the DC systems including the PV system. DC arc-fault can either be a

parallel fault (a short-circuit fault) or a series fault (an open-circuit fault). In PV

systems, the detection mechanism relies on backfed current to detect theses faults.

The nature of the faults, especially the series fault contravenes the logic in its

detection using current as the threshold metric. The difficulty in DC arc-fault

detection is compounded in PV systems, particularly at low irradiance which also

includes night to day transition and partial shading. The fast action of the maximum

power point tracking (MPPT) algorithm to put the system at different MPP operation

also imposes additional difficulties in the task of developing accurate reliable DC

arc-fault detection techniques.

In this thesis, a fault detection and diagnostic tool call the decomposed open-

closed alternating sequence (DOCAS) morphological fault detector (MFD) has been

proposed for application in fault detection in both AC and DC systems. The DOCAS

algorithm is a multistage morphological filter constructed from two nonlinear

Mathematical Morphological (MM) filters called the Morphological Median Filter

(MMF) and the Alternating Sequential Filters (ASF). The MM based technique

analyses the topography of the input signal waveforms by means of a probing signal

ivSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

called the structuring element (SE) in complete time domain. MM has the ability to

detect seemingly insignificant changes in the topography of the signal waveform

being investigated. The DOCAS algorithm uses a decomposed weighted SE to

enhance its performance in fault detection. The designed structure of DOCAS

algorithm allows it to be seamlessly applied in fault detection in both AC and DC

systems without any structural change. The characteristics of the MM technique

make the DOCAS algorithm convenient for the detection and classification of HIFs

as well as DC Arc-Faults in PV systems.

The performance of the DOCAS algorithm has been tested in radial

distribution feeder with PV based RE sources connected as DERs for short circuit

faults studies. From these studies, a strategy for adaptive radial distribution feeder

OC protection with built-in DC offset suppression capability is proposed. The

DOCAS algorithm’s capabilities in HIF detection and classification based on feature

extraction has been tested on various contact surfaces using the IEEE 13 bus test

system. The test results showed that DOCAS is capable of extracting successfully the

two target HIF features including randomness and arc extinction and re-ignition

characteristics. A strategy for HIF detection based on the extraction of the two target

features is proposed. The DOCAS algorithm was tested in a radial distribution feeder

with PV based RE sources as DERs for DC arc-fault detection on the PV side. The

performance of the algorithm has been remarkable with all cases of DC arc-fault

detected under all simulated conditions including low irradiance and changing

maximum power point (MPP).


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Table of Contents

Keywords .................................................................................................................................. i

Abstract .................................................................................................................................... ii

Table of Contents ......................................................................................................................v

List of Figures ........................................................................................................................ vii

List of Tables ......................................................................................................................... xii

List of Abbreviations ............................................................................................................ xiii

Statement of Original Authorship ...........................................................................................xv

Acknowledgements ............................................................................................................... xvi

Publications .......................................................................................................................... xvii

Chapter 1: Introduction ...................................................................................... 1

1.1 Overview of Power System Protection ...........................................................................2

1.2 Conventional Distribution Network ...............................................................................5

1.3 Problem Statement ........................................................................................................11

1.4 Research Aims ..............................................................................................................13

1.5 Research Questions .......................................................................................................14

1.6 Research Contributions .................................................................................................15

1.7 Significance of the Research in Bushfires and Wildfires Prevention ...........................16

1.8 Thesis Outline ...............................................................................................................18

Chapter 2: Literature Review ........................................................................... 21

2.1 Introduction ..................................................................................................................21

2.2 Radial Distribution Feeder OverCurrent Protection .....................................................22

2.3 High Impedance Faults (HIF) Detection and Classification .................................................34

2.4 DC Arc-Fault Detection in Photovoltaic Systems ........................................................41

2.5 Summary and Implications ...........................................................................................49

Chapter 3: Designing the Multistage MM Arc Fault Detection Algorithm . 54

3.1 Introduction ..................................................................................................................54

3.2 Research Methodology Utilizing the MM Technique ..................................................54

3.3 Background of MM Based Techniques ........................................................................55

3.4 design of the morphological algorithm for power system fault detection ....................63

3.5 Attributes of the DOCAS Algorithm ............................................................................71

3.6 Conclusion ....................................................................................................................82

Chapter 4: Adaptive Overcurrent Protection in Active Radial Distribution Feeders with

RE Based DERs ................................................................................. 84

4.1 Introduction ..................................................................................................................84

viSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

4.2 Thevenin Equivalent Parameter Estimation .................................................. 85

4.3 Effect of PV System Penetration on Feeder Substation Fault Current Level .............. 88

4.4 Effect of Fault Location on Feeder Substation Fault Current Level ............................ 91

4.5 DOCAS Algorithm in Adaptive Overcurrent Protection of Radial Distribution Feeder

with PV Penetration ..................................................................................................... 94

4.6 Application of MFD Output in Adaptive Radial Distribution Feeder OC Protection 102

4.7 Simulations and Discussion ....................................................................................... 104

4.8 Inverse-Time Over Current Relaying Using MFD Output Signal ............................. 122

4.9 Conclusion ................................................................................................................. 126

Chapter 5: HIF Detection and Classification in Distribution Feeders ............................. 129

5.1 introduction ................................................................................................................ 129

5.2 Proposed Method for HIF detection and Classification ................................................................ 130

5.3 Application of the MFD Output in Detection and Classification of HIF ................... 132

5.4 Simulations and Discussions ...................................................................................... 136

5.5 Challenges in HIF Detection in the Presence of Increasing RE based DER in Radial

DistrIbution Networks ................................................................................................ 156

5.6 Conclusion ................................................................................................................. 164

Chapter 6: DC Arc-Fault Detection in PV Systems ...................................... 167

6.1 Introduction ................................................................................................................ 167

6.2 The Proposed DC Arc-Fault Detection Technique .................................................... 167

6.3 Simulations and Discussions ...................................................................................... 169

6.4 Conclusion ................................................................................................................. 183

Chapter 7: Conclusions and Future Directions ............................................. 185

7.1 Summary of Conclusions ........................................................................................... 185

7.2 Future Directions. ....................................................................................................... 188

Bibliography ........................................................................................................... 205


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List of Figures

Figure 1.1: Typical connection of protective devices .................................................. 3

Figure 1.2: Functional character of the protective devices .......................................... 4

Figure 1.3: Typical structure of the conventional power system. ................................ 5

Figure 1.4: Radial Distribution Feeder ........................................................................ 6

Figure 1.5: Radial distribution feeder with PV system DERs ................................... 11

Figure 2.1: A typical feeder overcurrent protection scheme ...................................... 26

Figure 2.2: Functional blocks of a Time Overcurrent Relay ..................................... 27

Figure 2.3: Relay states for power system fault detection ......................................... 28

Figure 2.4: Standard inverse-time overcurrent relay characteristic curves at

TDS =1 .................................................................................................... 30

Figure 2.5: The first Emanuel HIF arc model ............................................................ 39

Figure 2.6: Modified Emanuel HIF arc model ........................................................... 40

Figure 2.7: TACS HIF arc model ............................................................................... 40

Figure 2.8: A typical PV system configuration with MPPT ...................................... 42

Figure 2.9: Series and parallel connection of PV modules in typical PV array ........ 44

Figure 3.1: Physical effect of (a) dilation and (b) erosion ......................................... 60

Figure 3.2: Physical effect of (a) opening and (b) closing transforms ...................... 61

Figure 3.3: Eccentrically decreasing convex structuring element. ............................ 66

Figure 3.4: DOCAS Response at the MMF stages (a) Simple AC input signal,

(b) MMF average output and (c) Difference fault signal, ∆f .................. 72

Figure 3.5: Fault detection windows of the DOCAS MFD output signal .................. 74

Figure 3.6: DOCAS response to SLG fault, (a) Fault current waveforms, SLG

fault on phase A, (b)-(d) corresponding MFD Outputs for each

phase. ....................................................................................................... 77

Figure 3.7: DOCAS response to SLG fault (a) Fault voltage waveforms for

SLG fault on phase A, (b)-(d) corresponding MFD outputs for each

phase. ....................................................................................................... 77

Figure 3.8: The MFD tall edge spikes for current, (a) MFDTall (1) and (b)

MFDTall (2) The MFD tall edge spikes for current, (a) MFDTall (1)

and (b) MFDTall (2) .................................................................................. 77

Figure 3.9: The MFD tall edge spikes for voltage, (a) MFDvTall (1) and (b)

MFDvTall (2) ............................................................................................. 78

Figure 3.10: DOCAS output for HIF arc extinction and re-ignition feature (a),

voltage signal and current signals, (b) fault voltage and HIF

current, and (c) MFDv output showing target MFDArc spikes .............. 80

viiiSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

Figure 3.11: DOCAS response to DC arc-fault in PV systems, (a) DC arc-fault

voltage, (b) Average MMF output, (c) diff DC fault voltage, ΔV

and (c) MFD output. ............................................................................. 82

Figure 3.12: DOCAS response to DC arc-fault in PV systems, (a) DC arc-fault

current, (b) Average MMF output, (c) diff DC fault current, ΔI

and (c) MFD output. ............................................................................. 82

Figure 4.1: A typical radial distribution feeder with PV penetration ......................... 85

Figure 4.2: PV system sequence networks ................................................................ 87

Figure 4.3: Per phase circuit diagram of the typical radial feeder system with

PV ............................................................................................................ 90

Figure 4.4: Per phase circuit diagram categorising PV sources into downstream

and upstream sources with respect to point of fault. ............................... 94

Figure 4.5: The OC fault detection and diagnostic scheme incorporating the

DOCAS algorithm. .................................................................................. 95

Figure 4.6: Flowchart showing the OC fault detection process ............................... 104

Figure 4.7: Test feeder for modelled in Simulink for simulations ........................... 105

Figure 4.8: Characteristic curves for the PV strings at STC, (a) I-V and (b) P-V

curves ..................................................................................................... 107

Figure 4.9: Circuit topology of a DC-DC boost converter [171] ............................. 107

Figure 4.10: Switching waveforms for the voltage and current in the DC-DC

boost converter .................................................................................... 108

Figure 4.11: Characteristic curves for the PV strings at STC, (a) I-V and (b) P-

V curves with increased temperatures ................................................ 109

Figure 4.12: Circuit topology of two-level voltage source converter with a

phase output voltage waveform [179] ................................................. 112

Figure 4.13: Fault current signals with DC-offset, (a) The fault current signal

and average MMF output (b) Difference fault current signal. ............ 113

Figure 4.14: Power spectral density plots for (a) fault current (b) average MMF

output and (c) difference fault current, ∆i. .......................................... 113

Figure 4.15: Difference fault current signal, ∆i and the EWMA filter output ......... 114

Figure 4.16: Power spectral density of the EWMA filter output ............................. 114

Figure 4.17: RLSE filter signals (a)RLSE filter input signal, (b) RLSE filter

output signal ........................................................................................ 114

Figure 4.18: RLSE filter magnitude response for SLG fault at fault location 2 at

0%, 28% and 33% PV penetration ...................................................... 117

Figure 4.19: MFD output corresponding to fault current magnitude for SLG

fault at fault location 2 at 0%, 28% and 33% PV penetration. ........... 117


fault at fault location 2 at 0%, 28% and 33%, 44% and 50% PV

penetration levels. ............................................................................... 119


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Figure 4.21: RLSE filter magnitude response for SLG fault at fault locations 1,

2 and 3 at 28% .................................................................................... 120


fault at fault locations 1,2 and 3 at 28% PV penetration. ................... 120


fault at fault location 2 at (a) 0% and 28% PV penetration. ............... 122

Figure 4.24: Standard moderately inverse ITOC relay curve with M values in

Table 4.10 ........................................................................................... 124

Figure 4.25: ITOC relay curves at various TDS values ........................................... 126

Figure 5.1: Structure of the Morphological HIF detector ........................................ 130

Figure 5.2: MFD fault windows partitions for HIF detection .................................. 131

Figure 5.3: Flowchart of Proposed HIF Detection and Declaration ........................ 136

Figure 5.4: IEEE 13 bus test system ........................................................................ 137

Figure 5.5: Emanuel Arc model in HIF simulation.................................................. 138

Figure 5.6: V-I characteristic curves of the simulated contact surfaces .................. 139

Figure 5.7: MFDvWindow spikes for SLG fault ......................................................... 141

Figure 5.8: MFDvWindow spikes for capacitor switching .......................................... 141

Figure 5.9: MFDvWindow spikes for induction motor switching ............................... 141

Figure 5.10: MFDvWindow spikes for step load increase ........................................... 141

Figure 5.11: Signals for HIF at 602 on conc. surface (a) fault voltage and HIF

current, and (b)MFDv output .............................................................. 146

Figure 5.12: MFDvTall edge spikes for HIF at 602 on conc. surface (a) MFDvTall

(1), (b) MFDvTall(1) pu increase,(c)MFDvTall(2) and (d)

MFDvTall(2) pu increase. ..................................................................... 146

Figure 5.13: MFDvShort edge spikes for HIF at 602 on conc. surface (a)

MFDvShort (1), (b) MFDvShort(1) pu increase,(c)MFDvShort(2) and

(d) MFDvShort(2) pu increase. .............................................................. 147

Figure 5.14: Random MFDvWindow spikes for HIF at 602 on conc. surface ............. 147

Figure 5.15: MFDvArc spikes for HIF at 602 on conc. surface, (a) MFDvArc

spikes and ............................................................................................ 148

Figure 5.16: MFDvTall edge spikes for HIF at 605 on dry grass (a) MFDvTall (1),

(b) MFDvTall(1) pu increase,(c)MFDvTall(2) and (d) MFDvTall(2) pu

increase. .............................................................................................. 152

Figure 5.17: MFDvShort edge spikes for HIF at 605 on dry grass (a) MFDvShort

(1), (b) MFDvShort (1) pu increase, (c)MFDvShort(2) and (d)

MFDvShort(2) pu increase.. ................................................................... 152

Figure 5.18: Random MFDvWindow spikes for HIF at 605 on dry grass .................... 152

Figure 5.19: MFDvArc spikes for HIF at 605 on dry grass, (a) MFDvArc spikes

and ....................................................................................................... 153

xSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

Figure 5.20: Signals for HIF at 602 on conc. surface (a) fault voltage and HIF

current, and (b) MFDv output with noise. ........................................... 154

Figure 5.21: MFDvTall edge spikes for HIF at 602 on conc. surface (a) MFDvTall

(1), (b) MFDvTall(1) pu increase,(c)MFDvTall(2) and (d)

MFDvTall(2) pu increase with noise. .................................................... 155

Figure 5.22: MFDvShort edge spikes for HIF at 602 on conc. surface (a)

MFDvShort (1), (b) MFDvShort(1) pu increase,(c)MFDvShort(2) and

(d) MFDvShort(2) pu increase with noise. ............................................. 155

Figure 5.23: Random MFDvWindow spikes for HIF at 602 on conc. surface with

noise. ................................................................................................... 155

Figure 5.24: MFDvArc spikes for HIF at 602 on conc. surface, (a) MFDvArc

spikes and ............................................................................................ 156

Figure 5.25: Radial feeder with PV .......................................................................... 157

Figure 5.26: DOCAS MFDi outputs for HIF at XF1 closer to the feeder

substation (a) MFDi without PV, (b) MFDi with PV .......................... 158

Figure 5.27: DOCAS MFDi outputs for HIF at XF2 further from the feeder

substation (a) MFDi without PV, (b) MFDi with PV .......................... 158

Figure 5.28: HIF signals for HIF at XF2 on dry grass (a) fault currents for with

and without PV, (b) MFDi outputs for with and without PV .............. 161

Figure 5.29: MFDiTall spikes for HIF at XF2 on dry grass (a) MFDiTall (1) spikes

for with and without PV, (b) MFDiTall (2) spikes for with and

without PV .......................................................................................... 162

Figure 5.30: MFDiTall spikes and MFDiTall pu increases for HIF on dry grass at

XF2 with PV (a) MFDiTall (1) spikes, (b) MFDiTall (1) pu increase,

(c) MFDiTall (2) spikes, (c) MFDiTall (2) pu increase ........................... 162

Figure 5.31: HIF signals for HIF at XF2 on dry grass (a) fault voltage with PV,

(b) MFDv output .................................................................................. 162

Figure 5.32: MFDvTall spikes and MFDvTall pu increases for HIF on dry grass at

XF2 with PV (a) MFDvTall (1) spikes, (b) MFDvTall (1) pu increase,

(c) MFDvTall (2) spikes, (c) MFDvTall (2) pu increase .......................... 163

Figure 5.33: MFDvShort spikes and MFDvShort pu increases for HIF on dry grass

at XF2 with PV (a) MFDvShort (1) spikes, (b) MFDvShort (1) pu

increase, (c) MFDvShort (2) spikes, (c) MFDvShort (2) pu increase ........ 163

Figure 5.34: Window, MFDvWindow spikes ................................................................ 163

Figure 5.35: HIF arc extinction and re-ignition, MFDvArc spikes ............................ 164

Figure 6.1: Block diagram of the DC Arc-Fault detection system ........................... 168

Figure 6.2: Radial distribution feeder with PV penetration used in the

simulation study .................................................................................... 169

Figure 6.3: PV array configuration in the simulation system .................................. 170

Figure 6.4: A typical PV system configuration array configuration ....................... 170

Figure 6.5: Characteristic curves for the PV String configuration (a) I-V curve,

and (b)P-V curve ................................................................................... 171


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Figure 6.6: Layout of the PV modules in the PV strings for fault simulations. ....... 171

Figure 6.7: Current signal measured at DC bus with associated signals for DC

arc-fault FP1 ........................................................................................... 176

Figure 6.8: Voltage signal measured at DC bus with associated signals for DC

arc-fault FP1 ........................................................................................... 176

Figure 6.9: MFD outputs for PV string currents for fault DC arc-fault FP1 ............ 176

Figure 6.10: Current signal measured at DC bus and MFD out for DC arc-fault

FP7 ....................................................................................................... 177

Figure 6.11: Voltage signal measured at DC bus and MFD out for DC arc-fault

FP7 ....................................................................................................... 177

Figure 6.12: Current signals measured at PV strings for DC arc-fault FP7 ............. 178

Figure 6.13: MFD outputs for PV string currents for DC arc-fault FP7 .................. 178

Figure 6.14: Current signal measured at DC bus and MFD out for DC arc-fault

Fs2 ....................................................................................................... 179

Figure 6.15: Voltage signal measured at DC bus and MFD out for DC arc-fault

Fs2 ....................................................................................................... 179

Figure 6.16: Current signals measured at PV strings for DC arc-fault Fs2 .............. 179

Figure 6.17: MFD outputs for PV string currents for DC arc-fault FP2 .................. 180

Figure 6.18: DC arc-fault current and MFD output high to low transition after

fault ..................................................................................................... 181

Figure 6.19: DC arc-fault voltage and MFD output high to low transition after

fault ..................................................................................................... 181

Figure 6.20: DC arc-fault current and MFD output high to low transition before

fault ..................................................................................................... 182

Figure 6.21: DC arc-fault voltage and MFD output high to low transition

before fault .......................................................................................... 182

Figure 6.22: DC arc-fault current and MFD output high to low transition with

fault ..................................................................................................... 183

Figure 6.23: DC arc-fault voltage and MFD output high to low transition with

fault ..................................................................................................... 183

Figure 7.1: A typical DC power distribution system ............................................... 199

xiiSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

List of Tables

Table 2.1: CTS of a non directional ITOC relay ........................................................ 30

Table 2.2: Common V-I relationships in DC Arc models.......................................... 48

Table 4.1: Prefault current MFD values ................................................................... 115

Table 4.2: Fault current MFD values at fault locations along feeder length. ........... 115

Table 4.3: Increase in fault current magnitude at different fault location ................ 116

Table 4.4: MFD values fault currents for faults at Fault location 2 ......................... 116

Table 4.5: Fault current increase at various PV levels for faults at location 2 ......... 117

Table 4.6: Increase fault current magnitude at different PV level for fault a

location 2 ............................................................................................... 118

Table 4.7: Fault level increase at different fault location at 28% PV penetration .. 120

Table 4.8: Increase in fault current magnitude at different fault distance 28%

PV .......................................................................................................... 122

Table 4.9: Per unit increases (M) in fault current magnitude at different PV

levels at FP2 ........................................................................................... 125

Table 4.10: Per unit increases (M) in fault current magnitude at different fault

locations ................................................................................................. 125

Table 4.11: Trip times at different PV levels ........................................................... 125

Table 4.12: Trip times at different fault distance ..................................................... 125

Table 5.1: Contact surfaces in HIF simulations ....................................................... 138

Table 5.2: Time duration for existence of the MFD spikes due to non HIF

transients ................................................................................................ 140

Table 5.3: HIF Detection and Time of Appearance of MFDv spikes for HIF

Feature Extraction ................................................................................. 145

Table 5.4: Prefault MFDv Values ............................................................................. 150

Table 5.5: Revised MFDv Threshold limits at different fault locations ................... 151

Table 5.6: Threshold Values at Different SNR Values ............................................ 153

Table 5.7: MFDiTall edge values for HIF at location XF2 .......................................... 160

Table 6.1: Prefault current measurements for PV strings at different irradiances ... 173

Table 6.2: Load current under simulated fault conditions at different

irradiances ............................................................................................. 174

Table 6.3: Calculated backfed current for the simulated fault conditions ............... 175

Table 7.1: Summary of OC Protection Schemes for Distribution Networks with

Increased DER Penetration ................................................................... 194

Table 7.2: Summary of Protective Devices used DC Distribution Systems ............ 200


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List of Abbreviations

ACR Automatic Circuit Recloser

ASF Alternating Sequence Filter

ASF Alternating Sequential Filter

CB Circuit Breaker

COASF Close-open Alternating Sequence Filter

CT Current Transformer

CTS Current Tap Setting

DER Distributed Energy Resource

DFT Discrete Fourier Transform

DMMF Decomposed Morphological Median Filter

DOCAS Decomposed Open Close Alternating Sequence

DWT Discrete Wavelet Transform

ESS Energy Storage System

FFT Fast Fourier Transform

GFPD Ground Fault Protection Device

HIF High Impedance Fault

IEEE Institute of Electronics and Electrical Engineers

IMPP Current at Maximum Power Point

ITOC Inverse Time Overcurrent

MFD Morphological Fault Detector

MFDi Morphological Fault Detector output for current signal input

MFDv Morphological Fault Detector output for voltage signal input

MM Mathematical Morphology

MMF Mathematical Median Filter

MMF Morphological Median Filter

MPP Maximum Power Point

MPPT Maximum Power Point Tracking

OC Overcurrent

OCASF Open-close Alternating Sequence Filter

OCPD Overcurrent Protection Device

PCC Point of Common Coupling

xivSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

PV Photovoltaic

RE Renewable Energy

RLSE Recursive Least Square Error

RMPP Resistance at Maximum Power Point

RMS Root Mean Square

SE Structuring Element

SS Substation

STC Standard Test Condition

TDS Time Dial Setting

TMS Time Multiplier Setting

VMPP Voltage at maximum Power Point

VT Voltage Transformer

WT Wavelet Transform


Resources xv

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature: QUT Verified Signature

Date:

xviSmart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources

Acknowledgements

It gives me pleasure in taking this opportunity to express my sincerest

appreciation and gratitude to those who have generously contributed and helped me

throughout my PhD research that is presented in this thesis.

In Particular, I would like to express my heartfelt gratitude to my principal

supervisor, Dr Yateendra Mishra who has provided expert guidance and spent

countless hours in very worthwhile discussions and encouragements to get me

through some challenging times. I also acknowledge all the time he spent in

providing very good reviews of draft thesis, journal and conference papers which

have eventually been published.

I also extend my sincere gratitude to my associate supervisor, Professor

Mahinda Vilathgamuwa for the time in providing good feedback and corrections to

my thesis.

I would also like to gratefully acknowledge the support of Queensland

University of Technology for the tuition scholarship and providing the facilities and

resources during my PhD study. I also extend my sincere gratitude to the Papua New

Guinea University of Technology for the financial support in meeting all my living

expenses while I was pursuing my PhD studies.

Last but certainly not the least; I would like to specially thank my wife and my

children for having the patience to put up with my long absence while I pursued my

PhD research. I would like to also thank God, my mother and my father for bringing

me into this world.


Resources xvii

Publications

Peer Reviewed Journals (Manuscripts Published /Accepted for Publication)

1) M. Kavi, Y. Mishra, and M. Vilathgamuwa, “Morphological Fault Detector for

Adaptive Overcurrent Protection in Distribution Networks with Increasing

Photovoltaic Penetration, “IEEE Transaction on Sustainable Energy”,Vol.9,

No.3, pp. 1021-1029, Jul., 2018. DOI: 10.1109/TSTE.2017.2759158

Contribute to Chapter 4 of the Thesis

2) M. Kavi, Y. Mishra, and M. Vilathgamuwa, “High Impedance Fault Detection

and Classification in Power System Distribution Networks Using Morphological

Fault Detector Algorithm, “IET Generation, Transmission and Distribution”,

pp.3699-3710, May 2018. DOI: 10.1049/iet-gtd.2017.163


Peer Reviewed Conferences (Manuscripts Published /Under Review)

3) M. Kavi, Y. Mishra, and D. Vilathgamuwa, "Detection and identification of high

impedance faults in single wire earth return distribution networks," in

Proceedings of Australasian Universities Power Engineering Conference

(AUPEC), 2016, pp. 1-6, Nov. 2016. DOI: 10.1109/AUPEC.2016.7749341

4) M. Kavi, Y. Mishra, and M. Vilathgamuwa, "Challenges in high impedance fault

detection due to increasing penetration of photovoltaics in radial distribution

feeder," in Proceedings of IEEE Power & Energy Society General Meeting (PES

GM), 2017, pp. 1-5, 2017. DOI: 10.1109/PESGM.2017.8274658


5) M. Kavi, Y. Mishra and M. Vilathgamuwa, “DC Arc-Fault Detection in PV

System Using Multistage Morphological Fault Detection Algorithm”, IEEE Ind.

Electronics Society Conf. (IECON) 2018, Washington. DC, Oct 2018. (Accepted

15th August 2018).


https://doi-org.ezp01.library.qut.edu.au/10.1109/TSTE.2017.2759158

http://dx.doi.org/10.1049/iet-gtd.2017.1633

https://doi-org.ezp01.library.qut.edu.au/10.1109/AUPEC.2016.7749341

https://doi-org.ezp01.library.qut.edu.au/10.1109/PESGM.2017.8274658

Chapter 1: Introduction 1

Chapter 1: Introduction

This chapter introduces the topic of the thesis and provides background

discussion related to the topic including general overview of distribution network

protection, its components and their functional attributes. The technical challenges

introduced due to the increased penetration of RE based DERs in distribution

network feeder protection are discussed. The discussions further highlight the

coexistence of AC and DC systems within the distribution network made possible by

the advances in power electronic converters and interfacing technology, and the need

to provide effective protection in both systems.

The current trend in the design and utilisation of modern distribution networks

includes diverse generating sources including renewable energy (RE) sources

directly connected to the distribution feeders as distributed energy resources (DERs).

The inclusion of RE based DERs changes the passive unidirectional power delivery

nature of the radial distribution networks to active networks with bidirectional

current flow. The conventional OC protection system in radial distribution feeder

relies on current magnitude as a threshold metric, thus it is imperative that sufficient

fault current magnitude above the predefined threshold limit during fault must exist

for this scheme to be effective. The direct interconnection of RE based DERs at the

distribution feeder contravenes this fundamental requirement as the DERs supply

power to distributed loads along the feeder length thereby reducing the current

supply emanating from the feeder substation. This can affect the coordination of the

protective devices in the feeder OC protection scheme hence compromising its

effectiveness in responding to fault conditions reliably.

Furthermore, other fault conditions such as high impedance faults (HIFs)

resulting from fallen conductors as well as energised conductors making unwanted

contacts with tree trunks and branches are quite common in medium (MV) and low

voltage (LV) distribution networks. Unlike OC faults, this category of faults

generates a low current magnitude rendering the feeder OC protection scheme

ineffective in detecting such faults. This inherent difficulty can be exacerbated by

increased levels of RE based DERs in the distribution feeders.

2 Chapter 1: Introduction

In recent times, the advances in power electronic converters and interfacing

technology has enabled the creation of DC subsystems within the AC distribution

networks to directly supply DC power to DC loads from DC sources such as

photovoltaic(PV) based RE sources or through AC-DC inverters from traditional AC

sources. This adds further challenges in designing protection systems for DC systems

as the protection mechanism in AC systems cannot be applied in DC systems.

These technical challenges introduced due to the increased penetration of RE

based DERs define the scope of the research problem. The aims and objectives of the

research, the research questions and the research contributions are also defined in this

chapter.

1.1 OVERVIEW OF POWER SYSTEM PROTECTION

The Power System Protection scheme is the design and interconnection of

specialized measurement, decision making and isolating devices whose function is

the fast detection and isolation of any abnormalities posing immediate threat to the

reliability, security and continuous operation of the power system. A power system

protection scheme must have the following designed attributes to meet the protection

system functional requirements [1]:

Speed: The protection system must operate rapidly to interrupt the fault to

minimise damage or possible system collapse. Intentional delay can be

introduced as part of the relaying strategy to coordinate between zones

of protection. The minimum time taken to isolate hazard is called the

clearing time.

Sensitivity: This ensures that fault (current/voltage) magnitude (however small)

should be detected by the protection system. The protection system

must be able to correctly discriminate between fault and normal

operating condition based on comparison with some predefined

inequality constraint or threshold quantity.

Selectivity: This is defined by the relaying or switching strategy where only the

portion of the network or equipment under fault is isolated. Relaying

strategy sectionalises the network into zones of protection whereby only

faulty equipment or network portion within a zone of protection are

isolated. Zones of protections are defined by proper grading of


protection threshold, time delay and/or operating characteristics of the

protective relaying devices.

Security: Security of the power distribution network operation means that the

protection system must be reliable. Moreover, robustness is also

anticipated as it would add to the notion of reliability of the protection

system. Reliability defines the expectation that the protection system

devices will correctly operate when expected to. Robustness suggests

that the devices can operate reliably under dynamic network condition.

The security of the protection system further is enhanced by having

backup protection. If a protection device in a zone fails to operate, then

backup protection is provided by devices in the neighbouring zones.

1.1.1 Basic Components of Protection Scheme and Their Functions

A typical power system protection scheme includes the following functional

devices; measurement devices, decision-making device and isolating device [2]. A

simple interconnection of these devices while not implying any protection scheme

implementation is shown in Figure 1.1. A protection system generally has three basic

elements such as 1) current transformers (CT) and voltage transformers (VT), 2)

Relays and 3) CB as illustrated where the CT and VT are the measurement devices.

The relay is the decision-making device that actuates the circuit breaker (CB) which

is the isolating device.

Figure 1.1: Typical connection of protective devices

The protection system continuously monitors the state of the power system by

taking measurements of quantity such as voltage (V´) and current (I´) via the

secondary windings of the VT and CT respectively. The voltage (V) and current (I)

signals are the primary quantities, other quantities, such as impedance, admittance


and power, etc., are secondary quantities can be computed from the primary

quantities if required to execute a decision. The functional attributes of the protection

components are graphically illustrated in Figure 1.2. The occurrence of any

abnormality or disturbance is detected by comparing the measured and/or computed

quantities against a preset threshold metric. The network is said to be in normal

(healthy) state and is allowed to continue operating if the measured quantities and/or

their derivatives are within the limit defined by the preset threshold metric. If the

value of the measured quantities and/or their derivatives violates the limit of the

threshold metric, the network is said to be in abnormal (unhealthy) state. The

protection system goes into state of alert to indicate abnormal network state, and the

decision logic element is activated to issue a trip command to take out the faulty

system apparatus or isolate the faulty section of the network and allow service to the

rest of the network.

The functional block diagram in Figure 1.2 shows that, the protection relay is

the key component in defining the functional attributes of the protection system. The

protection relay executes the decision to send appropriate trip signal to the associated

circuit breaker to isolate the unhealthy part of the network and allow service

continuity to the rest of network. The relay senses the existence or occurrence of any

abnormal condition, then in collaboration with the circuit breaker to isolate the

disturbances in a fast and reliable manner to minimise any harmful consequence to

the power system as well as human beings. Thus, to ensure the security, integrity and

reliability of a power system, it is highly imperative to have a fast and effective relay

operation that is sensitive and respond rapidly to hazardous condition to isolate any

faulted equipment or section of the network under any operating conditions and

allow the continuous operation of the healthy section of the network.

Figure 1.2: Functional character of the protective devices


1.2 CONVENTIONAL DISTRIBUTION NETWORK

The power system generally consists of Generation, Transmission and

Distribution subsystems as shown in Figure.1.3 with typical voltage levels either

stepped up and/or down by appropriate transformers. In the conventional power

system, the power is delivered to the users from the power generation plants at some

remote/isolated location through intricate network of step-up transformers,

transmission lines, step-down transformers at substations and distribution lines [3].

The power is delivered to the consumers who are connected to the power

system network through radial distribution feeders continuously and reliably. To

ensure reliability and security of operation of the power system for optimum

operation in power delivery under any operating conditions, there must exist

sensitive and fast responding protection system. Protection systems are integral parts

of the entire power delivery system. The conventional distribution networks operate

at medium voltage (MV) to low voltage (LV) and consist of radial distributions

feeders for consumer connectivity as tapped load along the feeder length [1]. The

feeders originating from the distribution substation have lateral branches through

which consumers are connected to by means of step down distribution transformers

that provide either one- or two- phase circuits.

Figure 1.3: Typical structure of the conventional power system.


Considering the system referred to in Figure 1.3, the distribution feeders exist

at the part of the distribution subsystem at 10-40 kV. The feeders are radial in nature

and provide means to distribute energy to the consumers as tapped loads through

distribution transformers. A typical example of a radial distribution feeder with step-

down transformers supplying power to the consumers as tapped loads is shown in

Figure 1.4. The Substation source represents the HV primary source in the

distribution substation which is stepped down to 10-40 kV. The consumers are

connected through distribution transformers at lower voltages of between 220-400 V.

Figure 1.4: Radial Distribution Feeder

1.2.1 Challenges in Modern Distribution Networks

The conventional radial power distribution network is constrained by some

major operational drawbacks such as low efficiency in power delivery. The power

generating plants are located at some distance, hundreds of kilometres away from the

consumers, and power is carried through the transmission and distribution lines that

span the entire distance. This requires upgrading aging power system infrastructure

and expansion of the transmission and distribution systems. Moreover, the long

distance increases the transmission loss resulting in low efficiency. Furthermore, the

primary energy sources in such centrally located power systems are fossil fuel based

such as coal and diesel. Such conventional energy sources are unsustainable and

environmentally destructive, contributing to greenhouse gas emission and increasing

global warming. The need to overcome the existing constrain compounded with ever

increasing demand for energy, and specialised characteristics of some loads such as

DC loads dictates, more so necessitates the need to transform the power supply

system [4], [5].

The transformation and/or reconfiguration of today’s power system are

intended to overcome the drawbacks of the conventional power system. In the


modern power system design, there are proposals to address the issues of low

efficiency and, high maintenance and operational costs by integrating small scale

power generating systems at the distribution network as DERs [6] coupled to radial

distribution feeders. Direct integration of DERs reduces the requirement for long

transmission lines, which reduces maintenance and operational costs, and improve

energy efficiency by reducing transmission losses and better management of

resources. While achieving improvements in cost reduction and energy efficiency, it

is envisaged that the transformed power system will meet the obligation for reduced

carbon emission. This requirement is inevitable and mandatory, thus must be

incorporated into the design and implementation of the modern power system as

international governments develop and adopt policies to reduce global warming.

Furthermore, with the advancement in power electronic technologies there are

increasing number of load types that require DC power supply. Therefore, to supply

both AC and DC load demands, the modern power system must be more flexible,

and configurable energy system [7]. This view has seen the proliferation in

production and deployment of RE based DERs and energy storage systems (ESSs)

[8] at the distribution network. Some RE technologies that have matured and widely

used include wind turbines [9], photovoltaic (PV) arrays [10] fuel cells and micro-

turbines [11] while ESSs include batteries [12], flywheels [13] and super capacitors

[14]. A power system that exhibits such characteristics with integration of PV system

at the distribution feeder level is depicted in Figure.1.5.

1.2.2 Integration of RE Based DERs

The ever-increasing demand for energy, and more so the need for clean,

sustainable environmentally friendly energy sources has seen the proliferation in the

development and deployment of renewable energy harvesting technologies such as

photovoltaic modules, wind generators, fuel cells, etc., to name a few. Moreover,

with the advancements in the development of power electronic converters and

interfacing technologies, the RE sources can now be easily integrated into medium

voltage (MV) or low voltage (LV) distribution networks as DERs. Figure.1.5 shows

the integration of PV based RE sources as DERs into the radial distribution feeder.

The integration of RE based DERs such as PV systems provides opportunity for

diversification of load types that can be supplied directly or indirectly by the DERs.

The RE based DERs such as PV systems can supply DC loads directly through a DC-


DC converter as well as supply AC loads through DC-AC converter. There is

increasing load diversification today due to advances in power electronic devices

where more and more loads today require DC power supply as opposed to the

traditional AC power supply. Thus, the development trend in modern power system

must consider, and be capable of supporting the integration of diverse and increasing

penetration of RE based DERs, energy storage, electric vehicles while meeting the

high power quality standard required by sensitive modern digital devices and loads

[15], [16].

1.2.3 Challenges in Distribution Network OC Protection System Design Due to

Increased Penetration of RE Based DERs.

The existing power system, more so the distribution subsystem and its

integrated protection mechanism were not designed for high penetration of DERs,

more so renewable energy resources. The existing distribution network/feeder

overcurrent protection system was designed considering the passive, unidirectional

current flow. However, integration of the RE based DERs fundamentally changes the

distribution network from passive to active network with bidirectional current flow

[17]. Moreover, increased deployment of power electronic converters and loads

increases harmonic injection thus increasing the level of voltage and current

distortion. Furthermore, high penetration of RE based DERs contribute large

component of the total fault current thus reducing the magnitude of the fault current

at the feeder substation. This will adversely affect the main feeder protection relay at

the feeder substation to provide effective protection as the fault current could fall

below the overcurrent threshold [18] ,[19]. Further challenge to distribution network

feeder protection is that increased penetration of the RE based DERs will decrease

the reach of the protective devices within a zone of protection for which it was

configured to operate [20].

Some researchers have proposed that the future distribution network will

incorporate DC buses to connect DC sources and loads [21]. To maintain optimum

system operation, the DC generation sources as well as associated components and

loads must be protected from harmful fault conditions. The AC feeder OC protection

system cannot be used as it is not designed for the DC system protection. Thus, a

separate DC protection system is necessary for protection of DC systems. The

existing DC fault protection scheme which is contingent on detecting sufficient fault


current magnitude, [22] cannot guarantee protection against all types of faults in DC

systems. For instance, the DC system OC protection will fail to operate in PV system

under low irradiance as the fault current magnitude would be insufficient to trigger

the OCPD and/or GFPD.

In designing protection systems for AC and DC networks, consideration for

adaptability is a key factor. The dynamics of both the AC and DC systems including

network topology change under different operating conditions with RE penetration

must be considered. In this thesis, the study is based on PV as DER; however, it is

assumed that the analysis, observations and conclusions, and the outcome of the

research will be applicable to all RE based DERs unless specified otherwise. Thus, to

maintain generality, the use of PV based DERs will be dropped, instead RE based

DERs is used.

1.2.4 Faults on AC Systems

The RE based DERs when connected to the distribution network as shown in

Figure.1.5 form an integral part of the AC system, and they receive power from and

contribute power to the feeder. Power distribution networks including distribution

feeders are not immune to faults. Faults such as single line-to-ground (SLG), line-to-

line (LL), three phase (3Ph) faults, etc., can occur at any point in the network. These

kinds of faults are low impedance (short circuit) faults which generally result in high

fault current. In distribution feeder protection, the overcurrent protection strategy is

used to arrest any fault. This scheme is quite simple, and its effectiveness is

contingent on accurate measurement of the fault current magnitude. The measured

fault current magnitude is tested against a preset threshold parameter. The threshold

parameter in the conventional OC protection scheme is designed for passive network

with unidirectional current flow. The main feeder protection relay is normally

located at the feeder substation, and sufficient fault current magnitude, above the

threshold value must be detected by the relay to guarantee reliable protection. With

the penetration of RE based DERs along the feeder, the network is no longer passive;

it becomes an active network with bidirectional current flow. Under any short-circuit

fault conditions, the DERs have the potential to contribute fault current resulting in

reduced fault current magnitude at the feeder substation falling below the OC

threshold. Factors that impact on the fault current magnitude at the feeder substation

include the level of RE based DER penetration and the distance to fault from the


substation and the type of fault. The network topology change influenced by the

intermittency of the RE sources is another parameter that impacts on the fault current

magnitude. Thus, for the feeder OC protection scheme to be effective, the threshold

parameter must be adaptive to all the changes introduced by the RE based DERs.

High Impedance Faults: The power system network not only suffers from low

impedance faults, but also other fault conditions resulting in low fault current

magnitudes known as high impedance faults (HIFs) are prevalent in medium voltage

(MV) and low voltage (LV) networks. Because of the inherent low fault current

magnitude from HIFs, the conventional OC protection will become ineffective under

such fault conditions. HIFs are very difficult to detect because of the low fault

current magnitude as well as their highly random and nonlinear characteristics which

most often involves arcing.

In the face of the changing landscape in the distribution network brought about

by the integration of the DERs, more specifically the RE based DERs, effective

protection system, considering all fault types must be considered.

1.2.5 Faults on DC Systems

In the DC systems, all DC sources and loads are connected through the DC

bus. Faults can occur anywhere on such a system as well. Common types of faults on

the DC network involve parallel and series faults. Parallel faults are typically short

circuit faults, where accidental bridging between two conductors or bridging between

positive conductor and ground occur. The risk of such fault occurring in the DC

system is increased by degradation of conductor insulation and other contaminants.

Series faults on the other hand are typically open circuit faults resulting from break

or mechanical detachment of conductor due to poor solder connection, rusting etc.

These faults, more specifically parallel faults can be classified as mismatch faults in

PV systems. Generally, for both fault categories, if the condition is conducive for

arcing to occur, then arc ignition will occur, the arc will be sustained. Under this

condition, the faults are classified as parallel DC arc-faults and series DC arc-faults.

The protection system in DC systems utilizes the same principle as in AC

distribution network feeder OC protection. The deployment of the DC bus protection

scheme depends on placement of over current protection device (OCPD), which

essentially is a fuse in series with the load and ground fault protection device

(GFPD) to interrupt the fault current [23]. Essentially, the scheme depends on


reliable detection of large fault current, above a predefined threshold to operate the

protection devices. The DC arc-faults, particularly those occurring in the DC bus of

the PV system as well as on the PV strings, including inside the PV cells and

modules are difficult to detect, especially under low irradiance. The longer the fault

exists; the optimal operational condition of the DC system is degraded. Moreover,

the persistent and sustained DC arc increases the risk of fire.

Figure 1.5: Radial distribution feeder with PV system DERs

1.3 PROBLEM STATEMENT

The challenges and issues in power system protection considering the changing

landscape in distribution network due to the integration of RE based DERs are

summarised here, and they constitute the problems that motivated this research.


1.3.1 Challenges in Overcurrent Protection in Distribution Network with

Increasing DER Penetration

The conventional distribution network feeder OC protection has been designed

for passive radial network with unidirectional current flow. The penetration of

the RE based DERs at the feeder affects the conventional feeder OC protection

as well as introduce challenges in the redesign and implementation of feeder

OC in the following way;

▪ Distribution network feeder is no longer passive; it is dynamic with

bidirectional current flow.

▪ Network topology changes intermittently due to RE based DERs

switching in and out.

▪ Increased fault current contribution by the DERs affecting the feeder

OC relay threshold (pick up) parameter setting thus compromising the

relaying (switching) strategy.

▪ Prevalence of DC-offset and its effect on fault current magnitude

estimation for OC protection. This is an existing challenge that must be

considered.

1.3.2 Challenges in High Impedance Fault Detection in Distribution Network

High impedance faults are an existing phenomenon, whether with or without

DER penetration will always be difficult to detect. While research has spanned

decades, a universal HIF detection and classification algorithm is yet to be

developed. This is because of the challenges imposed by the characteristics of the

HIFs. These include;

▪ Low fault current magnitude typically, between 10-50 A which is

much lower than the OC protection threshold (pick up) parameter

setting. This makes it difficult or impossible for the OC protection

mechanism to respond to HIFs. Thus, requiring HIF detection

strategy that does not rely on OC threshold.

▪ The HIF current waveform is erratic and has asymmetrical positive

and negative half cycles with shoulder shape.


▪ The HIF current has high frequency harmonic components from 2 to

10 kHz

▪ HIF current build-up

▪ Non-stationary frequency spectrum

▪ Highly random, with non-linear voltage-current (V-I) characteristics.

No two HIF will exhibit same characteristics

▪ HIF characteristics are dictated by the contact surface, the network

condition, the environment and the weather condition.

1.3.3 Challenges in DC Arc-Fault Detection in DC Bus and PV Strings under

Low Irradiance

DC loads and DC sources are connected through a DC bus. In a PV system,

particularly on the DC side, fault can occur at the input to the inverter or at the DC

bus (bus formed by connecting the PV strings) or at the PV strings. The challenges in

DC Arc-Fault detection that forms the basis for the motivation in the DC arc-fault

detection proposed in this research include;

▪ The existing (conventional) DC OC protection scheme is incapable of

detecting DC arc-fault.

▪ There is no natural zero-crossing on DC system faults, including DC

Arc fault making it difficult in DC Arc-fault detection.

▪ DC Arc-faults are difficult to detect in PV systems, under low

irradiance, partial shading and day to night transition

1.4 RESEARCH AIMS

The aim of this research is to develop a Fault Detection and Diagnostic Tool

that can be used seamlessly in AC and DC distribution systems. The attributes of this

tool are then used to propose strategies for; Adaptive OC protection in distribution

networks with increased DER penetration, HIF detection and DC Arc-Fault detection

in PV systems which are defined herein.

1.4.1 Strategy for an Adaptive Feeder OC Protection Scheme

The feeder OC protection would have the following attributes;


1) Adaptive to the change in landscape of power system structure at the

distribution network.

2) Be versatile in the presence of bidirectional current flow and increased

fault current injection from the DERs resulting in lower current magnitude

at the feeder substation.

3) Having an OC threshold that is adaptive to the changing current magnitude.

4) Fast computation of fault current magnitude including suppression of the

exponentially decreasing DC-offset.

5) Must be able to detect power system fault and issue trip signal within 1

cycle of the fundamental frequency.

1.4.2 Strategy for HIF Detection and Classification

The attributes of the HIF detection and classification strategy would include;

1) Operate in tandem with OC protection system

2) Detect and classify HIF based on the HIF characteristics

3) Must be able to differentiate between a HIF and non HIF related

disturbances

4) Detect HIFs within a reasonable time delay

1.4.3 Strategy for DC Arc-Fault Detection in PV Systems

The attributes of the DC Arc-Fault detection in PV systems would include;

1) DC Arc-fault detection based on DC Arc phenomena that does not require a

threshold parameter. In other words, make use of the chaotic behaviour of

the DC arc phenomena to detect DC arc-fault.

2) Detect Arc fault under all conditions including, low irradiance, partial

shading, night to day transition.

3) Must be able to identify the faulted PV string

1.5 RESEARCH QUESTIONS

To meet the research aims, the following research questions are defined;


1) What is the effective strategy for fault detection and diagnosis that can be

applied in both AC and DC power networks with DC-offset suppression

capability in AC power system fault detection as well as convenience of

application in adaptive overcurrent protection in radial distribution feeders

with increased RE based DER penetration?

2) What are the analytical and computational methods for developing a fault

detection and diagnostic tool that can be seamlessly utilized in both AC and

DC power systems?

3) Are the analytical and computational tools capable of performing feature

extraction for high impedance faults detection?

4) Are the analytical and computational analytical tools capable of performing

feature extraction for DC Arc-faults detection?

1.6 RESEARCH CONTRIBUTIONS

The following are the contribution from this research:

-Tool for Fault Detection and Diagnosis: A fault detection and diagnostic tool

based on Mathematical Morphology for time-domain analysis of the fault signal

called the decomposed open close alternating sequence (DOCAS) morphological

fault detector algorithm is proposed. This tool is a multistage filter based on two

nonlinear morphological filters namely; the morphological median filter (MMF) and

the alternating sequential filter (ASF). The MMF filter is comprised of two cascaded

stages where the output of the first stage becomes the input to the second stage while

the ASF has two layers; the open-close and the close-open alternating sequential

filters each with four stages. The two layers of the ASF operate simultaneously, and

each stage of the ASF is cascaded in a hierarchical manner where the output of the

previous stage is cascaded to the next stage. This operational sequence is achieved

through the decomposition of the filtering signal call the structuring element (SE)

into two SEs used in the two different filters. The underlying nature and computation

technique in the DOCAS algorithm makes it possible for its application in both the

AC and DC power system fault detection and diagnosis. The complete process in

developing the DOCAS algorithm is presented in section 3.4.

-Methods for analysing impact of RE based DERs on fault current

magnitude and Adaptive Inverse Time Overcurrent Relaying for Radial


Distribution Feeders with RE based DERs: An analytical method by means of

Thevenin parameter estimation and two distance factors, distance to the RE based

DER and distance to fault, to analysing the impact of the level of DER penetration on

feeder current magnitude is proposed. Then a technique for adaptive pickup setting in

overcurrent relays in distribution feeder overcurrent protection schemes using the

DOCAS MFD output is proposed. Moreover, a technique for determining the relay

trip time using inverse time overcurrent relay based on the adaptive pickup parameter

is proposed. The techniques are presented in Chapter 4.

-Method for the Detection and Classification of HIFs: A technique for the

detection and classification of HIFs based on feature extraction using the DOCAS

algorithm was developed. The HIF features extracted from the DOCAS MFD output

include the HIF randomness and the arc extinction and re-ignition feature. The

feature extraction and HIF detection and classification are presented in Chapter 5.

-Method for DC arc-fault Detection: A technique for DC arc-fault detection,

with applications in PV systems at any level of irradiance using the DOCAS

algorithm was developed. The technique uses the DOCAS algorithm to generate

MFD spikes to the chaotic behaviour of the sustained DC arc when ignited. The

DOCAS algorithm generates MFD spikes in response to the rate of change of the arc,

and the spikes sustained if the DC arc-fault exist. The technique is presented in

Chapter 6.

All the contributions are aligned to overall objective of the title of the thesis

with specific research aims defined in subsections 1.4.1, 1.4.2 and 1.4.3 in section

1.4.

1.7 SIGNIFICANCE OF THE RESEARCH IN BUSHFIRES AND

WILDFIRES PREVENTION

Power system infrastructure, including switchgear and powerlines at all

subsystems of the power delivery system are constantly exposed to elements such as

changing weather conditions and ageing and are prone to damage of fault conditions.

In many circumstances the faults occurring on or involving powerlines have been

blamed as sources of catastrophic bushfires and wildfires resulting in substantial

destruction of properties and sometimes tragic loss of lives. For instance, on the 7th

of February 2009, major bushfire, known as the Black Saturday bushfires in the state

of Victoria in Australia destroyed lots of properties, livestock as well tragic loss of


173 lives. The Victorian Bush Fire Royal Commission identified electricity

distribution infrastructure as the cause of these fires [24]. The Royal Commission

based on its investigations made 67 recommendations of which 8 are directly to the

electricity distribution infrastructure. The Victorian government in response to the

recommendations of The Royal Commission established a Powerline Bushfire Safety

Task force to investigate the recommendations directly related to the electricity

distribution infrastructure. The Task force furnished a report on the 30th of

September 2011 to the Victorian government, and out of several recommendations,

one was for further research into improving safety by identifying and introducing

new technology and methods for reducing risks and preventing bushfires ignited by

the electricity distribution system.

Powerlines span several hundreds of kilo meters and are exposed to weather

and often come into contact with trees and vegetation. Fallen powerlines due to

support structure failure and/or powerlines coming into contact with vegetation

almost always result in arcing. Unlike short circuit faults, these conditions are

classified as high impedance faults (HIFs) and generate low fault current magnitudes

resulting in not being detected by the overcurrent protection system. The longer the

arcing fault conditions persist, the higher the risk of igniting fire and electrocution of

people. These conditions must be detected and eliminated before they escalate into

any catastrophic events like bushfire and wildfires.

Bushfires and wildfires are disastrous events that are bound to happen if the

arcing sources such as HIFs are not detected and removed. HIFs generally result in

low fault current magnitude thus making it extremely difficult for their detection

using the conventional overcurrent (OC) protection scheme. Thus, specialise

algorithm specifically for arc-fault detection including HIFs have to be developed

that can work in tandem with the conventional OC protection system. Thus, the

DOCAS algorithm satisfies this requirement. Moreover, the DOCAS algorithm can

be used as part of an online intelligent power line condition monitoring system, for

the detection of arcing events.

Fire in DC systems including PV based DERs is major cause for concern. The

DOCAS algorithm can be seamlessly utilised in DC systems to detect the

occurrences of DC arc-fault as fire prevention mechanism in DC systems including

PV systems.


1.8 THESIS OUTLINE

This thesis consists of seven chapters including Chapter 1 which covers the

Introduction where the problem statement, the research objectives, the research

questions and research contributions were defined. The overviews of the rest of the

chapters of the thesis are presented herein:

Chapter 2: This chapter reviews literature on the three topics defined in the

problem statement including, 1) Challenges in overcurrent protection in radial

distribution feeders with increasing penetration of RE based DERs, 2) Challenges in

HIF detection in distribution networks and 3) Challenges in DC arc-fault detection

on the DC bus in PV systems.

A background review on the existing practice in radial distribution feeder OC

current protection and relaying strategy, and the drawback in its non-adaptive nature

in the face of increasing DER penetration are discussed. Moreover, a discussion on

the prevalence of the exponentially decaying DC-offset that occurs during short-

circuits faults and its ramification on feeder OC protection is provided. Several

existing techniques proposed in the literature to overcome the drawback and

challenges including suppression of the exponentially decay DC-offset in feeder OC

protection are discussed. The discussion is concluded with justification for a new

fault detection and diagnostic tool that is adaptive to the changing distribution feeder

network in the presence of increasing DER penetration as well as having immunity to

the effect of the exponentially decaying DC-offset.

A background review on the characteristics of HIFs and the challenges in their

detection based on the OC protection scheme is discussed. The identifying

characteristics of HIFs and the proposed HIF feature extraction techniques proposed

in the literature are discussed. Moreover, since this research is based on modelling

and simulation, a discussion on the proposed HIF models used in simulation studies

is provided to determine the appropriate model used in this research. The topic

review is concluded with justification of the proposed HIF detection technique as

well as HIF simulation model used in this research.

A comprehensive review on PV systems as well as their integration to the

distribution network feeders is discussed. Furthermore, the types of the DC arc-faults

and their causes discussed. A background review on the existing DC bus protection

scheme in the PV systems and the challenges in DC arc-fault detection is discussed.


Then a review on the proposed techniques existing in the literature to overcome the

challenges in DC arc-fault detection is presented. Furthermore, a review on the

proposed DC arc models used in DC arc simulations is done to determine the model

for use in this research. The topic review is concluded with the justification for the

proposed DC arc-fault detection and the DC arc model used in the simulation studies

in this research.

Finally, a summary of the literature reviews and their implications in

developing a research methodology that answers the research questions, and thereby

achieving the research objectives is presented.

Chapter 3: This chapter describes the development of a universal multistage

MM based filter called the decomposed open-closed alternating sequence (DOCAS)

morphological fault detector algorithm in answering the research questions to

achieve the research objectives defined in Chapter 1. The mathematical fundamentals

of the MM based techniques and detail descriptions of the composition of the

DOCAS algorithm are presented. The attributes of the DOCAS algorithm in adaptive

feeder OC protection, HIF feature extraction for the detection and classification of

HIFs as well as DC arc-fault detection are presented through simulation of simple

AC current and DC arc current signals.

Chapter 4: In this chapter, the impact of increasing RE based DER penetration

on the feeder current magnitude and its implications on the feeder OC protection is

first analysed by, 1) theoretical analysis using Thevenin equivalent circuit modelling

and decomposition of the circuits into sequence networks to determine relationship

for the level of fault current injection under fault condition by the DERs, thus

reducing the level of fault current magnitude seen by the feeder relay. 2) A radial

distribution feeder with RE based DER penetration is modelled using Simscape

library objects in MATLAB/Simulink. Different fault conditions, including single

line-to-ground (SLG), line-to-line-to-ground (LLG) and three phase (3-Ph) faults

were simulated at different locations on the feeder length under different DER

penetration levels, and the results were analysed using the DOCAS algorithm to

verify the trend in fault current magnitude reduction due to RE based DER

penetration. Then a method for short-circuit fault detection with adaptive threshold

parameter and adaptive inverse time overcurrent (ITOC) relaying using the DOCAS


MFD output is proposed to overcome the challenges in feeder OC protection

influenced by the increasing RE based DER penetration.

The theoretical analysis and simulation result of the technique used in

suppressing the exponentially decaying DC-offset are also presented.

Chapter 5: In this chapter, the application of DOCAS algorithm in HIF

detection and classification using several classifiers using the MFD output signal is

demonstrated. Cases of HIFs were simulated using the IEEE 13 bus test system.

Moreover, different contact surfaces were simulated by randomly changing the

effective resistance and the voltage and current signal were recorded. Moreover, non

HIF conditions were simulated to differentiate the non-HIF characteristics from the

HIF characteristics. The DOCAS algorithm was used to analyse these cases to verify

the effectiveness of the DOCAS algorithm in HIF feature extraction and

classification based on the MFD output signal. A HIF detection and classification

technique based on two HIF characteristics, namely the randomness and the HIF arc

extinction and re-ignition characteristics is presented.

Chapter 6: In this chapter, a method for DC arc-fault detection on the DC side

of the PV DER system using the DOCAS MFD output is presented. The DC arc-fault

detector utilises the DOCAS algorithm with no changes to it, and different arcing

faults including parallel, series and mismatch faults were simulated and analysed.

The DOCAS algorithm showed superior characteristics in detecting theses DC arc-

faults even at low irradiances. Moreover, the robustness of the DOCAS algorithm in

detecting DC arc-fault is tested with active maximum power point tracking and

transition in irradiance from partial shading.

Chapter 7: In this chapter, the conclusions from the research finds are

provided. The significant research contributions are specified, and the benefits and

importance of the proposed methods are summarised. Finally, recommendations for

future research directions are suggested.

Chapter 2: Literature Review 21

Chapter 2: Literature Review

2.1 INTRODUCTION

The issues and challenges from the paradigm shift in reshaping the landscape

of the power distribution networks due to increasing levels of penetration of RE

based DERs at the radial distribution feeders are discussed in this chapter. The issues

and challenges including: 1) Challenges in overcurrent protection in radial

distribution feeders with increasing penetration of RE based DERs such as PV

systems, 2) Challenges in HIF detection in distribution networks and 3) Challenges

in DC arc-fault detection on the DC bus in PV systems are discussed.

The chapter begins with discussions on the existing radial distribution feeder

OC current protection including relaying strategy. The drawback in the existing

feeder OC protection system is that, the pickup OC threshold setting is fixed, thus

making it non-adaptive in the face of increasing DER penetration. A review of the

proposed methods in the literature aimed at providing an adaptive solution to the

fixed pickup OC relay setting is discussed. Furthermore, a discussion on the

prevalence of the exponentially decaying DC-offset that occurs during short-circuit

faults and its ramifications on feeder OC protection, and the proposed methods for its

elimination are presented.

Moreover, in power system distribution networks, occurrences of HIF are quite

common. Unlike short-circuit faults, HIFs generally results in low fault current

magnitudes, thus rendering the OC protection system incapable of detecting HIFs.

The methods for HIF detection and classification based on HIF feature extraction

rather than fault current magnitude have spanned decades and are continuing to be an

active part of research today. The highly random and illusive nature of HIF has

proven to be very challenging in developing a universal HIF detection and diagnostic

tool. However, over the years, new methods of HIF modelling and feature extraction

techniques have evolved. A review of the characteristics and challenges in HIF

detection, and the proposed techniques for HIF detection and classification existing

in the literature are presented.

Moreover, the integration of the RE based DERs at the distribution network

feeders have introduced new opportunity for load diversification such as DC loads

22 Chapter 2: Literature Review

which are gaining momentum due to the advances in power electronic converter

technologies. RE DERs such as PV systems are naturally DC power sources and

provide opportunity to develop DC power systems. However, such systems are

generally exposed to the elements such as weather, aging, etc., and thus not immune

to fault conditions. In the interest of optimising service continuity and prevent

damage to these systems, it is mandatory to have reliable and effective protection

against any form of faults including DC arc-faults. DC arc-faults are very dangerous

and can have devastating effect on the PV systems, let alone any DERs that supply

power to DC loads if not detected a timely manner. Discussions on this chapter are

extended by providing a review on the challenges in DC arc-fault detection and the

proposed techniques in DC arc-fault detection existing in the literature.

A summary and implications of the review are provided to justify the reasons

for the choice of the time domain signal processing technique called mathematical

morphology for the design of the composite algorithm for seamlessly application in

AC and DC power distribution systems fault detection.

The rest of the chapter is organised as follows: In section 2.2 the background

and overview of distribution network feeder overcurrent protection including

literature review are presented. Section 2.3 presents overview on the characteristics

of high impedances faults and literature review on HIF detection and classification

techniques and HIF arc models. In section 2.4, review on DC arc-faults and the

challenges in their detection are presented. Section 2.6 discusses the summary and

implications including discussions on the application of MM in detection and

diagnosis of abnormal conditions in power distribution networks.

2.2 RADIAL DISTRIBUTION FEEDER OVERCURRENT PROTECTION

The radial distribution network referred to in Figure 1.4 indicates that the

current is directional and emanating from the substation source through the main

radial feeder and distributed to all the users connected to it. The current flow must

always be maintained to guarantee service continuity to all connected users.

Fault conditions such as short-circuit faults occur in distribution network

feeders due to exposure to the elements such as lightning strike, falling trees from

bad weather as well as through unintentional human-caused accidents, etc. Thus, it is

imperative on the service provider to implement a reliable protection system to

minimize damage to network apparatus as well as the possibility of total power


outage from such conditions while allowing unaffected parts of the network to

continue receiving power.

The main type of protection scheme implementation in radial feeder can be a

simplistic system such as OC protection using the current magnitude as the threshold

metric (pick up parameter) in the OC relay. The conventional OC protection system

assumes unidirectional current flow as well as having a fixed pickup setting

determined through load flow study. The feeder OC protection system based on this

assumption has been designed such that the coordinated switching strategy will be

maintained for all conceivable states and network configurations [25]. The OC

threshold settings of the relays as implied is non-adaptive and the fixed value is

maintained irrespective of change in network conditions such as load changes and

network topological reconfiguration. The fixed threshold setting based on the

location of the protective device is to ensure that all protective devices including

protective relays within a switching strategy are coordinated. However, the

introduction of the RE based DERs in the distribution feeders upsets the operation of

the conventional OC protection scheme.

A schematic of a typical OC protection for radial feeder is shown in Figure 2.1

where the main relay (R) is configured to provide backup protection and maintain its

reach of coverage to the furthest point. However, the DER penetration at any point in

the feeder will require adaptive schemes to respond to changes in the conditions of

the power distribution network feeders [26]. In OC protection, the parameter that

needs to be made adaptive is the threshold metric. The OC threshold setting must

automatically adjust (adaptive) to reflect the changes in the network.

As pointed out, current is the common parameter in a radial distribution

system, and under fault conditions, the current magnitude can increase significantly.

Hence, current magnitude is naturally taken as a positive indicator of any fault

condition. Hence, overcurrent protection schemes widely use current magnitude as

the threshold metric to detect fault by testing it against a predefined parameter value

such as the pickup setting of the overcurrent relay. If the increase in current

magnitude seen by the relay is larger than its pickup setting, then a signal is issued to

the circuit breaker to isolate the fault [27].

Power system fault conditions are classified either as temporary (transient) or

permanent faults, and cause increase fault current magnitude. A simple

implementation of the radial distribution feeder system overcurrent protection is


largely non-directional which requires only detection of current magnitude without

the complexities of phasor measurement. This requires strategic placement of

protective devices such as, relays with associated circuit breakers, automatic circuit

reclosers (ACRs) and fuses to operate in a coordinated manner to mitigate the impact

of both temporary and permanent faults. A typical example of one such arrangement

is shown in Figure 2.1.

The fuses are placed on the primary side of distribution transformers for

overcurrent protection of lateral branches to protect against temporary as well as

permanent faults while relays are place on the main feeder. The main circuit breaker

(CB) at the substation and the ACRs have overcurrent relays to initiate tripping if

temporary as well as permanent faults happen along the feeder. The main CB located

at the feeder substation performs dual functions of substation transformer protection

as well as backup protection for the ACR at B. It can be further seen that, the main

feeder CB controlled by the main relay R if operated will de-energise the entire

feeder. Therefore, under this circumstance, the main feeder relay is providing

complete backup overcurrent protection for the feeder.

In maintaining an effective feeder OC protection, all the protective devices,

including fuses, ACRs and relays must be coordinated to maintain synchronised

switching or relaying strategy. For this to happen, and referring to Figure 2.1, load

flow and short circuit studies must be performed on the feeder to determine the

available fault current magnitude at all locations including the substation where

protective devices will be placed. These studies are performed to determine the fuse

rating as well as the pickup setting of the OC relay including the ACRs.

Selectivity: This is a functional characteristic of the OC relay, to only isolate

faulted equipment or network segment while allowing service continuity to healthy

network segments. In distribution feeder OC protection, selectivity is achieved

through sectionalizing the feeder into zones of coverage to achieve coordination by

strategically placing protective devices like relays and ACRs. The zones also define

the reach of the relay. Relays are designed to cover up to certain distance of the

feeder, and this distance is defined as the reach of the relay [28]. In Figure 2.1, zone

1 defines the reach of the main feeder relay, and similarly, zone 2 defines the reach

of ACRs at locations B. Faults outside of zone 1 are considered too small to be

picked up by the relay at A. Thus, the ACR at B with smaller pickup current

magnitude than the relay must clear such faults.


Radial feeder OC protection uses inverse-time OC relationship where the CBs

within the faulted zone must operate, with the one nearest to the fault operating first

at the time to trip depending on the selected inverse time overcurrent (ITOC) relay

curve. To apply the ITOC principle to the radial feeder of Figure 2.1, let’s first

generalise that, any relay and associated breaker on the right side with respect to

another set is considered as being downstream to that set, and by inference, the set on

the left is upstream to the set on the right. Thus, ACR at B is downstream to the

substation relay. Moreover, the notion of downstream also implies the direction of

current flow which is uni-directional from the substation source to the length of the

feeder. Supposing a fault occurs towards the end of the feeder at point XF, ACR at B

will register larger fault current magnitude followed by the relay R at the substation

(Location A). ACR at B must respond to isolate this fault. Coordination is achieved

through defining tripping times, particularly in the controlling relays for the main CB

and ACR. The OC relays and ARCs have trip time setting and the trip time is made

inversely proportional to the magnitude of the fault current. In other words, the larger

the current magnitude is, the faster the trip time is. This implies that the relay closer

to the fault will see larger fault current magnitude as compared to relays further away

from the fault location, thus allowing the relay closer to the fault to activate its

breaker first.

The effectiveness of any OC protection scheme, including radial feeder OC

protection scheme is contingent on the availability of sufficient fault current level

above the relay pickup setting to provide reliable protection. For the radial

distribution feeder, the maximum fault current seen by the main relay R or measured

at the feeder substation would occur, under any load and generating condition for a

fault nearest to the substation. Therefore, fault data is computed at the HV bus of the

distribution subsystem. Information on fault data determines the rating of the fuse

and fixed threshold setting of the relays for proper coordination as previously stated.

With DER penetration, the load is distributed and taken up by the DERs

consequently reducing the current flowing from the substation, thus reducing the

current magnitude seen by the substation relay. The implication is that the conditions

under which the entire OC protection has been designed for such as the threshold

parameter and the fuse ratings are affected and may not maintain the switching

strategy coordination.


Figure 2.1: A typical feeder overcurrent protection scheme

The integration of the DERs requires that the feeder OC protection must be

adaptive to the changing network condition to provide reliable and effective OC

protection. One of the aims of this research defined in section 1.4 is to develop a

strategy for an Adaptive Distribution Feeder OC protection, and the vehicle for such

implementation is the digital OC protective relay.

2.2.1 Feeder Overcurrent Protective Relaying

The effectiveness and reliability of any protection scheme to respond to any

threat is as good as its relaying or switching strategy. Protection relaying is the

science or engineering involving the design and configuration of switching devices,

to detect any abnormal power system condition, then perform coordinated switching

based on two inequality constraints, fault magnitude threshold and switching time

delay [29]. The OC relay is the device that performs these functions.

Generally, a protective relay is a switching device that activates its contact

based on a decision made through certain test conditions as implied in the schematic

diagram of Figure 2.2. The protective relay can be visualized as having two

functional components, the decision logic mechanism and the contact mechanism

which also includes the relay coil. Power systems protection relays have evolved

over the years from traditional electromagnetic to modern digital relays. This has

seen the transition of particularly the decision logic functional component from

hardwired logic to software algorithms. In this perspective, it can be visualised that, a


relay has software algorithm that drives the hardware/switching component based on

some predefined constraint parameters.

Input

currentDecision

Normally OpenRelay Contact

Time Overcurrent Relay

Time setting

Pickup setting

Trip

signal

Figure 2.2: Functional blocks of a Time Overcurrent Relay

The protective relays continuously monitor the condition of the power system

by reading voltage and/or current signals, comparing the measured quantity values

with threshold inequality constraint. The relay sends appropriate trip signal

depending on the state of the power system network. The relay determines the state

of the power system network as stated here [1] ,[27]: The operation of the relay to

determine the state of the power system can be graphically illustrated by the block

diagram of Figure 2.3.

Normal State: The power system is said to be operating at its normal state if there

is no sudden or abnormal change (increase/decrease) in the power

system current magnitude. Any normal operating condition that

resembles characteristics of abnormal state must be discriminated

by the relay. Referring to Figure 2.3, lets denote the measured

(input) current x, and constraint to test the measured current against

the pickup setting, Xm. The system is said to be in normal state if x

< Xm.

Abnormal State: The power system enters an abnormal state if x > Xm. This does

not necessarily mean a fault has occurred. It simply implies

abnormal current has been detected. The protection relay gets into

an alert mode to indicate presence of an abnormal condition.

Action State: A time delay, t is initiated and compared against an inequality time

constrain, Tm through the time setting. If conditions, t ≥ Tm, and x


> Xm exist, then fault is declared, and action outage is initiated to

isolate the faulting device or unhealthy network segment.

Restorative state: If the fault is of temporary nature, the breaker reclosure action is

initiated by the relay to restore the network state of operation to the

condition before the fault. If, however the fault is permanent, the

fault is appropriately cleared, and the power system is restored.

Figure 2.3: Relay states for power system fault detection

2.2.2 Feeder Overcurrent Relay Settings

The feeder OC protection is implemented using inverse time overcurrent

(ITOC) relays [29] .These types of relays have one input for current measurement

and two settings, pickup setting determines the fault current magnitude to which the

relay should pick up, and the time setting determine the relay time to operate [27] .

Supposing, x is the measured current magnitude, and Xm is the pickup setting, then

the severity of the fault is determined by the ratio (x/Xm). If (x/Xm) < 1, the system is

in normal state and normal load current flows through the feeder. If (x/Xm) > 1, the

system is experiencing abnormal condition and the relay should pick up. The more

severe the fault is the higher the (x/Xm) ratio. Therefore, to cover all faults in the

zone of protection, a pickup setting that can pick up fault current for all faults in the


region must be selected, and this is normally the first step in applying inverse time

OC protection.

2.2.3 Inverse Time Overcurrent Relay

Inverse time overcurrent relays provide the mechanism for protection

coordination in radial distribution feeders. The inverse time overcurrent relay

synergises well with the notion of faster relay operating time at higher fault current

magnitude and slower time for lower fault current magnitude. In electromechanical

relay, time settings, call time dial settings/time multiplier settings (TDS/TMS) of ½

to 10 are used to determine the relay operating time, were TDS of ½ being the fastest

and 10 being the slowest. Moreover, the TDS/TMS determine the time-current

characteristic curve of the relay used to determine the operating point of relay. The

TDS settings were used to mark the position of the rotating contact on the relay disc

(normally at reset position) with respect to the fixed contact. And the speed of the

rotating disc was proportional to magnitude of the current, where higher fault current

magnitude would result in faster disc rotation, thus less time for the rotating contact

to reach and make contact with the fixed contact to enable the trip signal to be sent.

Conversely, lower fault current magnitude resulted in slower disc rotation, thus

longer for the rotating contact to reach the fixed contact, thus resulting in an inverse

time current relation [29]. This concept is adopted by the digital relays where TDS

are programmed through the relay algorithm. In digital relays, any imaginable relay

curve can be generated. However, it must be noted that a lot of electromechanical

relay are still in use today. Thus, to maintain compatibility between the different

relay types, the digital relay TDS parameters must be standardized to have

compatible current-time characteristic curves.

Different equations to determine the time-current relationship in ITOC relays

based on exponential and polynomial equations were proposed [30],[31],[32]. To

standardise the digital relays to be compatible with the electromechanical relays, the

Power System Relaying Committee of the IEEE Power Engineering Society released

a standard, IEEE C37.112-1996 [33],[34] in 1996 that defines the characteristic

equations for the ITOC relays [35] . The equation in (2.1) defines the relay trip

characteristic [33] ;

( )1

At I B TDS

M

= +

− (2.1)


Where t(I) is the trip time of the relay in seconds. The other parameters in the

characteristic equation are; M = I/Ipickup is ratio that determines the relay pick up

point. The other parameters, A, B and ρ are current tap setting (CTS) constants that

determine the selected curve characteristics. The standard further defines three

standard relay curves, with Moderately Inverse, Very Inverse and Extremely Inverse

curves. The values for the constant parameters are given in Table 2.1. The standard

relay curves at TDS = 1 are given in Figure 2.4. Different trip times can be obtained

from these curves at different M = x/Xm = I/Ipickup values.

Table 2.1: CTS of a non directional ITOC relay

Characteristic A B p

Moderately Inverse 0.0515 0.1140 0.02

Very inverse 19.610 0.4910 2.00

Extremely inverse 28.200 0.1217 2.00

Figure 2.4: Standard inverse-time overcurrent relay characteristic curves at TDS =1

2.2.4 Radial Distribution Feeder OC Protection

The overcurrent (OC) protection strategy, from the foregoing discussion

implies, firstly accurate measurement of fault current magnitude, and then performs

comparison with a predefined OC threshold to determine if a fault has occurred. If a

fault has occurred, then the protection devices must respond in a coordinated manner

for fast and selective isolation of the fault anywhere along the feeder and laterals [36]

,[37]. The design and implementation of this strategy is effective as implied in radial

distribution network with unidirectional current flow, and this is the premise under


which the existing feeder OC protection system was designed for. However, coupling

of DERs to the distribution feeders alters the topology of the distribution network

making it a multi-source dynamic network with bi-directional current flow. The

effectiveness of the distribution feeder OC protection system is contingent on

accurate measurement of fault current magnitude, the penetration of DERs reduce the

current magnitude seen by the feeder substation OC relay both under normal and

fault conditions. Moreover, the presence of DC-offset during short-circuit fault can

further degrade the fault current magnitude resulting in loss of coordination among

protection system devices; thus, rendering the feeder OC protection system

ineffective.

Various proposals from different researchers exist in the literature proposing

strategies and techniques for an effective and adaptive OC protection system in the

presence of increasing DER penetration.

An adaptive protection scheme based on diving the existing distribution

networks into zones and maintain a load balance with the application of emerging

technologies for updating of network status has been proposed by Brahma et al. [38]

and Javadian et al. [39] . This would require high capital investment. Various authors

including Baran et al. [28], Mahat et al.[40], Chen et al. [41], Yazdanpanahi et al.

[42] and Su et al. [43] have proposed various methods of adaptive relay setting to

maintain protection coordination. The authors in [28] proposed an adaptive scheme

based on changing the pickup setting of the OC relay by estimating the fault current

magnitude using an iterative technique. The speed of convergence of the technique

will determine the overall response time of the scheme, and this has not been

reported. In [40], the authors proposed an adaptive selection of appropriate relay

operating curves based on state estimation technique for faulted section detection as

well as detection of fault magnitude under grid connected and island mode. Authors

in [41] proposed another adaptive relay setting based on the operating mode of the

wind generator as well as network topology change and reconfiguration. Authors in

[42] and [43] have proposed adaptive protection strategies based on controlling the

inverter interfaced DERs output current under fault conditions by detecting the

decrease in voltage at the DERs, thus reducing the fault current contribution by the

DERs, hence allowing the OC relay to operate correctly using the existing setting.

However, with network topological change the relay preset parameters must be

adaptive for this to be effective.


Shen et al. [44] proposed another algorithm based on dynamic Thevenin

parameter estimation to compute the estimated fault current magnitude for adaptive

relay setting. Kumar et al. [45] proposed a technique based on recursive discrete

Fourier transform for fundamental phasor estimation , and Fuzzy logic controller to

set relay trip setting . Costa et al. [46] proposed an algorithm based on boundary

wavelet transform for current phasor estimation OC protection. These proposed

methods as well as the previously mentioned strategies based fundamentally on

either measuring or estimating the prefault and fault current magnitudes. Presence of

DC-offset during fault will obviously affect the accuracy of phasor estimation, thus

impacting on the accuracy and effectiveness of the proposed schemes.

2.2.5 Exponential DC Offset Removal

Several different techniques have been proposed for the treatment of the effects

of the DC-offset. The widely proposed technique in the mitigation and elimination of

the DC-offset in the fault current signal require some form of filtering algorithm to

remove the DC-offset as highlighted in [47] and [48]. Most removal algorithms

based on, DFT, Kalman Filter, Morphological Filter, etc., require some form of

parameter estimations, particularly the inductance (L) and resistance (R) parameters

to estimate the Time-Constant, τ of the exponentially decaying DC-offset, the phase

angle and the amplitude. Depending on the algorithm, a full-cycle and two samples

or a half-cycle and one sample is necessary to estimate the required parameters [49],

and if the estimated parameter is matched with the preset value, then a total

elimination of the DC-offset occurs, otherwise the DC-offset can only be suppressed,

but not eliminated. Moreover, the computation for parameter estimation increases the

time delay in extinguishing the fault and can become unacceptable. A notable

contribution in the search for a fast and effective algorithm for the elimination of the

DC-offset is that proposal by Rahmati et al. [50]. This method is DFT-based where

the DC-offset can be reduced to an acceptable level by subtracting the odd and even

samples of the original phasor without estimating any parameter. This algorithm

greatly reduces the time delay associated with all those other algorithms. However

further analysis of this technique needs to be conducted for its real-time application

and robustness.

In summary, the conventional power system distribution network is a passive

network with unidirectional current flow supplying distributed loads along the feeder


length. Simple overcurrent protection system using inverse time overcurrent (ITOC)

relaying strategy is implemented for overcurrent protection. The pickup setting on

the overcurrent relay is non-adaptive, and is compared against a threshold metric,

usually the current magnitude to activate the circuit breaker in the event of a fault.

With the changing landscape in power system structure with the integration of RE

based DERs, the distribution network is no longer passive, but dynamic with

bidirectional current flow. This results in more fault current injection from the DERs,

thus reducing the fault current magnitude at the feeder substation where the main

protection relay is located. This causes problem in protection system coordination.

Thus, to overcome this problem, the threshold parameter setting of the overcurrent

relay must be made adaptive to the changing network operating conditions.

Moreover, the existing relay algorithms in power system protection are based

are predominantly based on Fourier or Wavelet Transforms. These are integral

transforms and assume periodicity of the current and voltage waveforms to obtain

correct average of the signal parameters for the detection of the fault. However, it is

well known that current and voltage waveforms experience transients during

disturbances. Furthermore, power system signals are not immune to corruption due to

inherent system noises such as Gaussian white noise, harmonic distortion, high

frequency transients from natural phenomena like lightning strike, and the DC-offset

in the fault current due to short-circuit conditions. Such conditions compromise the

integrity of the algorithm and may result in the failure of the protection system.

Moreover, integral transforms are linear filters; however, in the presence RE based

DERs, a degree of nonlinearity is introduced due to the presence of different sources

and their operating parameters. The inverter interfaced RE based DERs such as PV

systems are stochastic, and their switching in and switching out introduces harmonics

and nonlinearity. Such situations may cause operational failure in the protection

system. To ensure high degree of reliability and security for optimum operation of a

power system, there must be an effective, sensitive, fast and robust protection system

for active distribution networks.

A method that does not rely on any parameter estimation for DC-offset

suppression is desirable to reduce computational burden and preserve speed in fault

detection capability of the fault detection and diagnostic tool.


2.3 HIGH IMPEDANCE FAULTS (HIF) DETECTION AND CLASSIFICATION

Any power system fault conditions or events resulting in low fault current

magnitude not sufficient to overcome the threshold setting of the conventional

overcurrent protection scheme are classified as HIFs. Unlike short-circuit faults,

HIFs normally have high impedance fault path which limits the fault current

magnitude. Occurrences of HIFs are frequently experienced at medium-voltage

(MV) to low-voltage (LV) networks. HIFs can be grouped as, 1) fallen energized

conductor either broken or intact making unwanted electrical contact with high

impedance surfaces such as bitumen, concrete, sand, soil, etc., and 2) energized

overhead conductor making unwanted contact with tree trunk and branches [51].

Such contact surfaces provide a high impedance fault path to ground resulting in a

fault current magnitude of typically between 10-50 A rms with erratic waveform.

While the low HIF current magnitude may not pose immediate significant threat to

power system infrastructure; nonetheless, a fault condition exits. Moreover, if a HIF

is allowed to persist, it increases risk of fire and safety hazard for people. Moreover,

HIFs introduce harmonics, and can degrade the quality of power supply in the long

run therefore must be detected and removed within reasonable time [52].

2.3.1 Difficulties in High Impedance Detection using OC Protection Scheme

The overcurrent (OC) protection scheme in distribution network depends on,

accurate measurement of fault current magnitude, and then makes comparison with a

predefined threshold to determine the trip time. If the fault current magnitude falls

below the threshold, the event is ignored. Considering that the threshold in OC

protection is set at around 2-to-3 times the prefault load current, it is impossible for

any conventional overcurrent protection device, whose effectives is contingent on

secure and reliable detection of increased fault current magnitude above a predefined

threshold to detect HIFs to suffice [53]. Thus, a scope for HIF detection and

classification technique based, not only on fault current magnitude but combination

of other HIF characteristics exists.

2.3.2 Characteristics of High Impedance Faults

HIF characteristics are highly random and nonlinear which adds to the

challenges in developing HIF detection and classification using pattern recognition

and other feature extraction techniques. There are several factors including, ground


contact surface material, weather, humidity, network topology, load condition and

voltage level [54] that dictate the randomness in HIF characteristic. The most

influential of all are; 1) contact surface material, which gives the nonlinear voltage-

current (V-I) characteristic and, 2) surface humidity which dictates the level of fault

current magnitude [55]. Generally, HIFs most often result in arcing creating an arc

channel for the fault current to flow. The arc channel can be represented by an arc

resistance, Rarc, which is highly random and inversely proportional to the surface

humidity [56]. The arc resistance restricts the magnitude of the fault current through

the arc channel. The fault current is further restricted by the high impedance contact

surface, whose resistance can be represented by, Rx. This gives an effective fault

resistance, Rf = Rarc + Rx seen at the fault location. Different contact surfaces have

different Rx. which defines the non-linear (V-I) characteristic for each surface

material. Rarc and Rx are random and usually very high values, which defines the

randomness and chaotic nature of the fault resistance.

For arcing to occur, a separation (gap) between the energized conductor and

high impedance contact surface must exist. The gap can be represented by an air-gap

breakdown voltage, Vbr, which must be overcome by the conductor voltage for arcing

to take place. Considering that the conductor voltage is cyclic at 50Hz, there are two

zero-crossing every half cycle which the arc is extinguished at, then re-ignites at

about the positive and negative peaks. Moreover, Vbr retains random value each half

cycle resulting in asymmetrical fault current waveform with unequal positive and

negative half values stated in [57] and [58]. Furthermore, the fault current gradually

escalates to its maximum value due to slow initial contact separation, and maintains

this value for several cycles giving the build-up and shoulder characteristics of the

HIF current [59]. Moreover, the HIF current contains high frequency harmonics of

between 2-10 kHz [60].

Voltage and current signals are the primary quantities in fault detection, and

from the foregoing discussions, it can be concluded that HIFs do not manifest clearly

in voltage and/or current variation. Moreover, the discussion alludes to the fact that;

HIFs exhibit highly random and non-linear characteristics influenced by the

environment and network condition. Furthermore, the physical characteristics of HIF

are specific to the condition at which the fault occurred. This implies that it may be

impossible to detect all cases of HIFs based on a single technique that targets a single

HIF signature. Thus, for a secure and dependable HIF detection, an algorithm


incorporating multiple techniques targeting more than one HIF characteristic may be

necessary.

Researchers in the past have proposed methods for HIF detection based on

feature extraction. Some of the success in identifying the characteristics of the HIF is

attributed to these researches.

The systems for HIF detection are classified as either mechanical or electrical

[61]. Mechanical protection using forced grounding to activate the conventional

protection system was introduced by Wester et al. [62] however, this was abandoned

due to reliability issue and cost implication. Other methods such as lower threshold

settings were proposed by Sharaf et al. [63] which compromised relay coordination

resulting in nuisance and unwanted tripping. Electrical-based systems extract HIF

characteristics in the time-domain [64],[65], frequency domain [66] and time-

frequency domain [67]. Development in new and advance signal processing

techniques allowed for further improvement in the HIF detection and classification

process. The techniques involving expert systems and learning algorithms including;

Decision-tree based algorithm [68], Kalman Filter [69], artificial neural networks

[70],[71] and [72], fuzzy logic [73] and neuro-fuzzy [74] were proposed. Hybrid

frequency-time domain technique such as wavelet transform [75],[76],[77], and time

domain techniques such as Mathematical Morphology [78] have also been proposed.

Shen et al. [68] used a technique based on decision-tree to analyse the

harmonic current magnitudes; however, this does not perform well under noisy

environments. Samantaray et al. [69] proposed a technique based on Kalman filter

and support vector machine (SVM), which is based on statistical learning theory to

extract features of magnitude and frequency of the fundamental and some odd

harmonics components. The technique requires large data set for training which can

become problematic if good dataset is not available. The expert systems based on

neural networks are proposed by Eissa et al. [70] and Baqui et al. [71] and

probabilistic neural networks by Samantaray et al. [72]. These techniques are

sensitive to frequency change and require large training data set as well as building

complex nonlinear system by learning examples which be problematic with

insufficient data set or incorrect training. Etemadi et al. [73] proposed multiple

techniques including WT, fuzzy logic and ANN for HIF detection and feature

extraction, whereas a genetic algorithm based on WT, principal component analysis

and fuzzy logic is proposed by Haghifam et al. [74] . Bakar et al. [75] proposed a


technique based on wavelet transform, and this is further enhanced by Mahari et al.

[76] and Costa et al. [77] with wavelet packet transform and boundary wavelet

respectively. The effectiveness of WT is based on the appropriate selection of mother

wavelet. Moreover, the technique is not immune to noise and the effect of dc-offset.

Gautam and Brahma [78] proposed a technique based on Mathematical Morphology

where the transients on the fault voltage signal are analysed. However, the algorithm

is limited to detecting the transients occurring only at or near the positive and

negative peaks. As the rate of appearance of transients depends on the rate of change

of the effective HIF impedance which is highly random, there is no guarantee that

transients will occur at those points on the input signal and hence jeopardizing the

robustness of the algorithm. Moreover, the algorithm depends on slope detection, this

means presence of harmonics and noise will cause spikes in the output of the

algorithm which can be easily confused with HIF.

2.3.3 High Impedance Fault Models

Considering the highly random nature of HIF characteristics, performing

staged tests would unlikely capture all characteristics of the HIFs. This is because

HIFs exhibit characteristics specific to the network condition, the environment and

weather conditions as well as the contact surface. Hence, any staged HIF tests would

not produce the same results. Moreover, staging HIF tests are dangerous and require

specialized equipment. Therefore, to overcome this limitation, simulation of HIF

characteristics using HIF arc models seem favourable, as it is flexible, and can cover

different conditions such as contact surface and network conditions. Therefore, to

perform HIF simulation tests as accurately as possible, developing or selecting a HIF

arc model that captures most, if not all the defined HIF characteristic is highly

imperative.

Research to develop realistic HIF arc models have spanned decades. Most

research conducted are based on staged and laboratory tests to develop HIF arc

models that capture the HIF characteristics for use in HIF fault detection [79],[80]

.[81]. The HIF arc or generally arc modelling for that matter can be placed into three

categories. The first group uses arc physics to correlate the arc resistance by means

of empirical relationship between parameters such as arc length and arc currents [82]

,[83]. The relationship between these parameters is defined by the equation in (2.1)

[83].


0.8525arc

arc

LR

I= (2.1)

where Rarc is the arc equivalent resistance, L is the arc length, and Iarc is the arc

current. In practical sense, considering the highly random nature of HIF, this model

fails to capture that. To overcome this limitation, a second category of HIF arc are

proposed where the conductance of the arc is calculated by means of differential

equations [53],[84, 85]. The randomness parameter is introduced with a randomly

varying voltage parameter per arc length. This model is defined by the (2.2) [85];

( )0

( ) ( )1

( )

i t g tdg

dt u R i t l

−=

+ (2.2)

In (2.2), |i(t)|/(u0 + R|i(t)|)Ɩ represents the stationary conductance of the arc,

usually denoted G, and g and τ represent the instantaneous conductance and time

constant of the arc. The arc resistance and the arc length are respectively represented

by the T and L. The parameter, u0 is a constant voltage parameter per arc length that

can be randomised to capture the randomness characteristics. However, the solution

to this model relies on the assumption of a fixed arc length which is not always the

case especially with energised fallen conductor that swings.

A consensus was reached by researchers and reported by Vijayachandran et al.

[86] that any model that is proposed must encapsulate the following features of the

High Impedance Fault; arcing, unsymmetrical nature of the fault current, stochastic

nature of the fault current due to zero arcing period, harmonic and high frequency

components in the fault current, random and nonlinearity in the fault resistance.

A third category of HIF arc models are based on curve fitting technique where

electrical circuit components such as voltage sources, diodes, resistance and

inductances are connected to form a circuit that mimic the HIF current waveform

emerged. The first arc model in this category is the Emanuel arc model proposed in

1990 as shown in Figure 2.5 [87],[88]. This model was proposed based on laboratory

tests and measurements, and while it is simple, it attempts to characterise the

fundamental features of the High Impedance Faults. The schematic arrangement of

the diodes and the DC voltage sources models the unsymmetrical positive and

negative half cycle in the current waveform. Moreover, the randomness

characteristics are modelled by randomizing the dc voltage sources Vp and Vn in


positive and negative half cycle respectively. Furthermore, the rectification process

in switching from positive half cycle to the negative half cycle results in zero current

magnitude at the zero-crossing of the fundamental frequency which conveniently

models the arc extinction and re-ignition at zero-crossing in AC arc. Noting that, the

Vp, Dp combination in parallel with Vn, Dn combination forms are full-wave rectifier,

thus resulting in a full cycle current waveform with unsymmetrical positive and

negative peak values with shoulder shaped form. Moreover, the rectification process

introduces harmonics and high frequency components in the current which

reminiscent of the HIF current characteristics, thus modelling those phenomena.

Figure 2.5: The first Emanuel HIF arc model

However, it was noted that the single resistor model does not properly account

for randomness in positive and negative half cycle when in contact with a high

impedance surface. Thus, a modification to the original Emanuel arc model is done

by placing either one or two resistor variable resistors in series with the DC voltage

sources to model the randomness introduced by different contact surfaces

[68],[69],[73],[89] . One such model is shown in Figure 2.6. Another HIF arc model

structurally different from the modified Emanuel HIF arc model is shown in Figure

2.7. This model is referred to as the Transient Analysis of Control System (TACS)

model. The TACS model while structurally different, attempts to accurately capture

the characteristics of the HIFs. The TACS model incorporates a nonlinear resistance,

time-varying voltage sources connected in parallel, but only one effective in each

half cycle, and transient analysis of controlled system switch to connect and

disconnect the fault and randomly vary the fault resistance.

The third category of HIF models has found wide application in time domain

simulations of HIF conditions.


Figure 2.6: Modified Emanuel HIF arc model

Figure 2.7: TACS HIF arc model

In summary it can be said that, the most common HIF characteristic is AC

electric arc. Arcing most often follows the onslaught of HIFs, and it’s the arcing

phenomena combine with among others, parameters such as contact surface material

and weather condition that defines attributes of the HIF fault current. Most, if not all

proposed electrical methods attempt to extract features from the transients present on

the fault current induced by HIF for its classification. While many proposed

techniques exist in the literature for HIF detection and classification based on feature

extraction, a universal HIF detection and classification technique that combines


different signal processing techniques targeting different features of the HIF still

does not exist.

2.4 DC ARC-FAULT DETECTION IN PHOTOVOLTAIC SYSTEMS

In connected PV systems as DERs, PV modules are connected in series and

parallel combinations to form PV arrays. One common PV system configuration is

shown in Figure 2.8 where the associated controls as well as interfacing technologies

are shown. PV systems incorporate power electronics converters such as DC-DC

converters and DC-AC inverters allowing for simultaneous supply of DC and AC

loads respectively. PV systems can operate either as a stand-alone power systems or

inverter interfaced DERs at the distribution network connected through a delta-wye

transformer. When grid connected, the RE based DERs especially PV systems

become integrated subsystem of the distribution network as they feed power to and

absorb power from it during periods of high and low irradiance respectively. This

balance of operation is considered normal, steady-state operation, and must always

be maintained to maximize the benefits of the PV system.

While the integration of the PV system or any other RE system as DERs for

that matter seems to address issues on environmental concerns, energy efficiency and

maintenance cost reduction, it however introduces technical challenges in protection

against system abnormalities that would be detrimental to or compromise the safe

and reliable operation of the PV system [90],[91]. Occurrence or existence of any

anomaly must be quickly detected and isolated to ensure safe, reliable and stable PV

system operation. Considering the PV system in Figure 2.8, faults on both the DC

and AC sides of the network can affect the PV system operation. DC and AC side

faults differ quite significantly in characteristics and require different detection and

classification as well as isolation techniques. While generalized fault detection is a

matter of concern in DC power systems, this research is concerned with DC arc-fault

detection as especially in PV systems under low irradiance. The system in Figure 2.9

alludes to the existence of a DC link at voltage of about 300 V or higher using DC-

DC boost converter [92]. DC arc fault can occur anywhere in such system, either at

the PV strings or on the DC bus system. Detecting DC arc faults on the PV strings is

challenging, particularly at low irradiance levels which can be difficult to detect.


Typically, two types of arcing faults, parallel and series arcing faults as defined

in [93],[94] can occur on the DC side of the PV system. Typical DC arc-fault types

are elaborated herein.

Figure 2.8: A typical PV system configuration with MPPT

2.4.1 Parallel DC Arc-Faults

Typically, parallel faults are line-to-line short circuit faults that occur between

two points of unequal voltages of opposite polarities. This can be a bridge between

two lines (line-to-line) or two different points on the same line at different voltages

[95] . If a gap exists between the bridging conductors at the point of contact [96] a

DC arc can be ignited creating a route for the fault current through the restrictive arc

channel having resistance defined as arc resistance. This fault is classified as parallel

DC Arc-Fault. Moreover, the arc resistance limits the level of backed current from

the unfaulted PV strings which can be problematic in detecting the fault when fault

current is used as a threshold metric for fault detection. The probability of such faults

occurring is increased by exposed conductors due to insulator breakdown, exposed

dry solder joints, etc., making it possible for accidental bridging of conductors.

Parallel faults with arcing or otherwise occur in parallel to the load.

2.4.2 Series DC Arc-Faults

Unlike parallel faults, series faults are typically open-circuit faults; however, a

high resistive connection is maintained through the arc channel with limited backed

current through the arc resistance. Series arc faults usually result from mechanical

separation of conductor at the solder points or break in the conductor [97],[98] , and

occur in series with the load. Electrical discharge with arcing can occur between the


contact separation due to the presence of moisture or fluid on the conductors thus

providing a high restrictive fault current path.

2.4.3 Conventional DC Protection System in Photovoltaic Systems

Protection against dangerously high fault current on PV systems, particularly

on the DC side involves placement of overcurrent protection devices (OCPD) such as

fuses on each PV string as well as placement of ground fault protection devices

(GFPD) for PV system with ground connections [23],[99]. Supposing the PV system

in Figure 2.8 has PV array configuration as shown in Figure 2.9.

There are two configurations for PV protection systems, 1) is a grounded

system where the PV arrays and strings are grounded to system ground as illustrated

in Figure 2.9, and, 2) is an ungrounded or floating system which can be represented

by the same system, however without the ground point (G). In both cases, the

protection system depends on sufficient fault current to activate any protection

mechanism. Supposing the system of Figure 2.9, both grounded and ungrounded

configuration experiences a short-circuit fault on PV string (PVS1) at point F as

shown. The conventional protection system for such faults on PV systems relies on

the existence of a sufficient fault current level denoted IF. Supposing, IF_PV is the

fault current contribution from the faulted PV modules from the faulting PV string

and Ibf is the backed current from the unfaulted PV modules in the faulting PV string

and from the unfaulted PV strings, then the total fault current through the fault path

is, IF = IF_PV + Ibf. Supposing the type of fault is a parallel arcing-fault to ground,

then IG = IF = IF_PV + Ibf where both components of the fault current exist. The fault

current IF can be interrupted by either or both F1 at PVS1 and the GFPD at G. Now

supposing the system is ungrounded, and the fault is a parallel fault between two

strings as shown in Figure 2.9 between two points on strings PVS1 and PVS2

represented by the dashed arrow line. The protection in this case depends on F1 and

F2 on strings PVS1 and PVS2 respectively to interrupt the fault. In this scenario, the

fault current IF has only one component, the backfed current (Ibf). It is also logical to

observe that, even if the system is grounded, and fault is ungrounded the only

protection is from the OCPD devices which depends on sufficient Ibf to interrupt such

fault conditions. Thus, grounded parallel faults on a grounded system can be easily

detected and interrupted as opposed to ungrounded faults on both grounded system

as well as ungrounded system.


In the event of a series fault, IF = 0. However, if arc channel exists, then

IF = Ibf, but very much restricted by the arc resistance. Thus, in this research, only

ungrounded parallel and series arcing faults are considered. While grounded arcing

faults are not considered, it can be considered that, arcing fault through grounded

fault path is a case of parallel arcing fault with only the positive conductor contacting

grounded surface. Therefore, it envisaged that any DC arc detection technique

capable of detecting parallel arcing fault, more so series arcing fault under low

irradiance is equally capable of detecting ground fault under similar condition.

The only protection against onslaught from the fault current due to any fault on

an ungrounded PV system are the fuses installed on each PV strings, however only

the backed current is seen by the fuses. Therefore, any circuit component

intentionally installed that prevents the flow of backfed current such as blocking

diodes [99] or any fault condition that limits the flow of backfed current could

render the overcurrent protection devices (OCPD) ineffective for an ungrounded PV

system. In this research, it is assumed that blocking diodes are not installed; hence no

discussion on this is provided. The reliability of OCPD relies on sufficient fault

current magnitude to trigger the OCPD to operate. The fault current must be 2.1

times the rated short current of PV modules at standard test conditions (STC), 1000

W/m2, 25ºC. The following factors dictate the existence and level of fault current, 1)

irradiance, 2) type of fault, 3) location of the fault and 4) fault resistance [100].

Figure 2.9: Series and parallel connection of PV modules in typical PV array


2.4.4 Challenges in DC Arc-Fault Detection in Photovoltaic Systems

In any electrical system, faulted system component or network section is

isolated by selectively powering it down. In PV systems, this can be accomplished by

isolating DC supply (zero current and zero voltage respectively for series and parallel

arcing faults) to the inverter by means of DC-disconnect switches at the DC input of

the inverter [101],[102]. Unlike other energy sources, PV systems continue to

produce power even under fault so long as the modules are exposed to irradiance.

This can be very dangerous for emergency repair work to be carried out. Thus, any

arcing fault whether series or parallel arcing fault must be detected under any

environmental and operating conditions first then the faulted PV source isolated to

prevent risk of damage to the rest of the system.

In the proposed technique, it is assumed that sensors are installed at PV strings

to detect arcs by monitoring any change in PV string current to determine the

condition of the string. Protection against arcing faults in a PV system by use of

series connected fuse or any protection device that relies on increased fault current

magnitude to be activated will not suffice as the arcing faults do not increase the fault

current. Detection of arcing faults in PV systems is even more difficult particularly at

low irradiance, and from the foregoing, it is obvious that the fuse will be totally

ineffective. DC arc in PV systems can be ignited by accidental disconnection at

points along the PV string or bridging of active conductors between strings. The

separation or loose bridging creates a gap between the conductors, which initially

results in high incidence of electric field build-up that ignites arcing, and the arc is

sustained once ignited. It has been shown that any voltage magnitude greater than

30V is sufficient to ignite a DC arc. Thus, even at low irradiance, it is possible to

ignite arc at the PV strings.

Difficulties in DC arc detection is compounded by the fact that, naturally DC

arc current and/or voltage lack the zero-crossing attribute found in AC systems,

which make arcs in AC system to extinguish at zero-crossing and re-ignite after as

opposed to arc in DC system which are sustainable once ignited. The inherent non-

existence of natural zero-crossing [96] in DC signals prevents the adaption of AC arc

detection techniques based on that attribute for DC arc detection [103]. The DC arc

detection difficulty is compounded in PV systems, particularly at low irradiance

which also includes night to day transition and partial shading. The fast action of the

maximum power point tracking (MPPT) algorithm to put the system at different


MPP operation also imposes additional difficulties in the task of developing accurate

reliable DC arc-fault detection techniques.

The persistent DC arc-fault is dangerous and poses serious safety and fire

hazard if not extinguished. Moreover, the DC arc-fault can degrade the efficiency of

the PV system. Considering the challenges and safety issues highlighted herein, fault

protection, more so DC arc-fault protection in PV system using fault current

magnitude alone as the actuating signal for the OCFPD will obviously will not

suffice. While various methods have been proposed for DC arc-fault detection in DC

electric circuits in electric vehicles, sea and air crafts, etc. [104],[105],[106], a scope

for development of an accurate, reliable and cost effective generalized DC arc-fault

detection system that can be applicable to all DC electric systems, including PV

systems considering the challenges highlighted exists.

2.4.5 DC Arc Fault Detection Techniques

DC arc fault detection techniques based on DC arc signature characterization

have been proposed in the past. Yao et al. [107] and Telford et al. [108] proposed

different DC arc feature extraction and detection techniques for series DC arc-faults.

The authors in [107] proposed a hybrid technique combining time-domain analysis

for the DC arc chaotic characteristic and discrete wavelet transform (DWT) to

determine the correlation between energy increase and frequency during arcing. And

in [108] the authors proposed a series DC arc-fault diagnosis technique based on a

class of machine learning technique known as hidden Markov model to train the

proposed system using the extracted features of the series DC arc-fault. While these

proposed techniques are well developed, however, their effectiveness in series DC

arc fault detection in PV systems under low irradiance has not been evaluated, and

thus remains inconclusive.

Shimakage et al. [109] and Platon et al. [110] proposed DC side fault detection

by analyzing the measured AC side signal deviation from its estimated value.

Ducange et al. [111] proposed a method for detecting faults in PV modules by

comparing the estimated power and measured power in the faulted PV modules.

Chouder et al. [112], Silvestre et al. [113] and Spataru et al. [114] proposed

techniques to detect different operating conditions of the PV arrays to differentiate

abnormal from normal conditions, including partial shading, MMPT errors as well as

shorted PV modules. A method for comparing energy level of PV systems based on


statistical method was proposed by Vergura et al. [115], while Ando et al. [116] and

Guerrieoro et al. [117] proposed the use of wireless sensors to detect the changing

operating condition of PV system for fault detection. Yi et al. [118] proposed a

technique based on DWT/Fuzzy logic, and Kuo et al. [119] proposed a technique

based on time-domain analysis to determine the correlation between fractional-order

dynamic error and PV power degradation for fault detection under low irradiance in

PV systems. However, DC arc-faults were not considered in both cases, hence their

performance under such fault conditions remain inconclusive. More methods based

on time-domain approach have been proposed by Chae et al. [120], Haeberlin et al

[121], Strobl et al. [122] and Wang et al. [123] to statistically analyse variation in PV

system voltage and/or current signal for arcing fault detection. While all these

proposed methods contribute to advancement in knowledge towards the design and

implementation of suitable protection strategy for DC power system faults more so,

DC arc-faults, their practical implementation and versatility to adapt to changing

operating conditions such as changing irradiance have not been fully evaluated.

2.4.6 DC Arc Models

The research work involved in developing this thesis has involved simulation

of different test conditions. This approach was selected to have flexibility in

analysing different arcing faults under varying environmental conditions such as

changing irradiance. Therefore, to perform DC arc-fault simulation tests as

accurately as possible, developing or selecting an appropriate DC arc model is of

paramount importance.

With DC power systems gaining popularity, the momentum is on the rise to

develop adequate protection scheme for DC systems. Thus, to this effect, proper

modelling of DC arc is essential in analysing DC arc-faults in DC systems including

PV systems. DC Arc-faults when ignited create resistive arc channel with high

resistance that allow enough current to sustain the arc. Thus, there exist a relationship

between voltage and current (V-I) to determine the nonlinearity in DC arc resistance.

Hence, DC arc models have been modelled empirically based on staged tests by

curve fitting technique to determine the nonlinear V-I relationship. A summary of the

common V-I relationships based on the review by Ammarman et al [124] given by

[125], are presented in Table 2.2.


The V-I relations almost resemble each other, where, L is the gap length (which

was taken as an estimate of the arc length), and the A, B, C, D are parameters to be

determined experimentally. Stocks and Oppenlander [126] performed the most

exhaustive tests for free burning series arc in open air and recorded exponentially

decreasing currents from 1000 to 0.1 A. Based on this work, a formula for series DC

arc voltage was derived as given in (2.3).

( ) 1.1220 0.534arc arcV g I= + (2.3)

where g is the gap length given in millimetres, and Iarc is the arc current. Using (2.3)

Ammarman [124] developed a model for calculating the DC arc current using the

model for DC rms steady-state arc resistance in terms of the arc current as given in

(2.4)

( )0.88

20 0.534arc

arc

gR

I

+= (2.4)

where s

arc

s arc

VI

R R=

+

Table 2.2: Common V-I relationships in DC Arc models

Name Equation Experimental condition

Ayrton arc

arc

C DLV A BL

I

+= + + Carbon electrodes

Steinmetz ( )

0.5arc

arc

C L DV A

I

+= +

Carbon and magnetic

electrodes

Nottingham arc n

arc

BV A

I= +

n is related to the electrode

material, L = 0.039 to 0.39 in

Paukert arc b

arc

aV

I=

L = 0.039 to 7.78 in

Iarc = 0.3 to 100 kA

Modified Paukert arc b dL

arc

a cLV

I +

+=

L = 0.04 to 0.12 in

Iarc = 3 to 25 A

In summary, considering these challenges in the reliable detection and

identification of DC arc fault in PV system using fault current magnitude alone as the

actuating signal will obviously will not suffice. There is still a need for development


of an accurate, reliable and cost effective generalized DC arc detection system that

can be applicable to all DC electric systems, including PV systems.

2.5 SUMMARY AND IMPLICATIONS

A summary of the literature reviews and their implications towards addressing

the research questions to achieving the research objectives is presented herein:

2.5.1 Summary

In consideration of the dissimilar voltage and current signal attributes in the

domain of AC and DC voltage signals, a computational technique based on time

domain analysis is determined to be most suitable for developing a fault detection

and diagnostic tool that can be seamlessly applied in both AC and DC power

systems. The functional characteristics of MM to detect insignificant variations in the

topography of the graph of the fault signal makes it suitable for detecting changes in

voltage and current signal due to HIFs and DC arc-faults which are both arcing faults

but in the domain of AC and DC signals.

Radial Distribution Feeder Adaptive OC Protection: The integration of the

RE based DERs at the radial distribution network feeders changes the conventional

distribution network from passive network with unidirectional current flow to active

dynamic network with bidirectional current flow. The preceding discussions on

radial distribution feeder OC protection have shown that, increased RE based DER

penetration results in increased current contribution consequently reducing the fault

current level seen by the OC protection relay thus having the potential to

compromise the effectiveness of the OC protection system coordination. Moreover,

during short circuit faults, the fault current is normally affected by the high

frequency transients as well as the prevalence of the exponentially decaying DC-

offset. While the transients can be effectively filtered by means of low pass filtering,

the DC-offset on the other hand is not so easily removed. Its presence can affect the

fault current magnitude, which the feeder OC protection system is contingent on to

mitigate the damaging effects of short circuit faults and any other conditions

resulting in abnormally large current magnitudes.

The existing relay algorithms in power system protection including distribution

feeder OC protection relays are predominantly based on integral transforms such as

Fourier or Wavelet Transforms to estimate the fault current magnitude. The integrity


of these algorithms can be affected by the DC-offset. Moreover, the existing DC-

offset suppression techniques require filtering that require some form of parameter

estimation which can increase overall time delay in estimating the fault current

magnitude.

Therefore, to overcome these inherent difficulties, a fault detection and

diagnostic tool that does not relay on integral transforms is needed. Furthermore, a

method that does not require any parameter estimation is necessary for the DC-offset

suppression while preserving computational efficiency to reduce fault detection

delay. The method proposed by Rahmati et al. [50] meets this criteria, and will be

adopted in the design and development of the fault detection and diagnostic tool.

High Impedance Fault Detection and Classification in Distribution

Networks: While all the proposed HIF detection and classification methods have

short comings, they contribute to the advancement in knowledge towards

understanding the complex nature of the HIFs. As more research continues,

combination of different techniques and methods could evolve into the development

of a universal system for reliable HIF detection and classification.

Considering the physical characteristics of HIFs, the most common is AC

electric arc. Arcing most often follows the onslaught of HIFs, and it’s the arcing

phenomena combine with among others, parameters such as contact surface material

and weather condition that defines attributes of the HIF fault current. Most, if not all

proposed electrical methods attempt to extract features from the transients present on

the fault current induced by HIF for its classification. The HIF current has time

varying frequency spectrum which makes most frequency-based systems using

Fourier transform (FT) not suitable. Advance techniques using Kalman filters to

estimate the frequency components have been proposed [127]. Amongst the signal

processing techniques, WT/DTW holds the most promise. WT is a time-scaling

technique where both frequency information and point of fault inception are captured

and mapped in time. This makes WT effective in analysing frequency spectrum with

time-varying characteristics [128],[129]. WT based methods target transients induced

by the fault event. Since the transients due to HIF are subject to the damping on the

contact surface material, and if the fault input signals for feature extract are taken at

the distribution network substation, it could result in availability of insignificant

transients which will compromise the effectiveness of WT based methods. Moreover


Ghaderi et al. [52] reports that, despite having significant advantages, WT based

techniques are subject to 1) narrow frequency support, 2) selection of appropriate

mother wavelet, and 3) loss of feature resolution. Thus, despite active research, scope

for the design of a universal HIF detection scheme that combines different signal

processing techniques target different features of the HIF still exists.

The algorithm proposed in this research uses time domain analysis by means of

mathematical morphology to extract features from the HIF current to detect and

classify HIFs based on two HIF identifying characteristics, 1) randomness, due to

randomly changing the effective fault resistance, Rf giving the erratic fault current

and, 2) arc extinguishing and re-ignition around the fundamental period giving a

shoulder shaped unsymmetrical fault current waveform.

Furthermore, the model selection for the HIF detection in time-domain

simulation is the Emanuel arc model. This model, while yet simple captures the HIF

characteristics. Moreover, its simple circuit structure makes it convenient to

implement in MATLAB/Simulink which is the simulation environment of choice in

this research. Furthermore, different contact surfaces can be modelled by

appropriately setting the values of the parallel resistance in the model.

DC Arc-Fault Detection in Photovoltaic Systems: Considering the challenges

highlighted from the literature review, fault protection, more so DC arc fault

protection in PV system using fault current magnitude alone as the actuating signal

will obviously not suffice. While various methods have been proposed for DC arc-

fault detection in DC electric circuits in electric vehicles, sea and air crafts, a scope

for development of an accurate, reliable and cost effective generalized DC arc

detection system that can be applicable to all DC electric systems, including PV

systems considering the challenges highlighted exists. A DC arc-fault detection

technique based on time domain analysis using mathematical morphology is

proposed. Moreover, the choice of DC arc-fault model for generating arc current in

the simulation is that proposed by Ammarman as wide range of DC arc current can

be simulated.

2.5.2 Implications

The desired research outcome is to develop a universal fault detection and

diagnostic tool that can be utilised in distribution feeder adaptive OC protection as

well as having the ability to detect HIF based on feature extraction. Moreover, the


fault detection and diagnostic tool must have the flexibility to be easily customised

for application in DC arc-fault detection in PV system without requiring major

changes. In fault detection and diagnostic systems, the primary input signal quantities

are normally current and voltage. AC and DC voltages and current do not exhibit the

same attributes, thus a signal processing technique that can be utilised in capturing

fault information in the domain of both AC and DC signals is desired. A time domain

analysis-based fault detection and diagnostic tool based on Mathematical

Morphology is proposed. A review of the proposed application of MM based

techniques in power system fault detection and condition monitoring is provided to

show that it is maturing technique and can be utilised as an alternative to the

traditional methods for power system fault detection and diagnosis.

2.5.3 Application of MM in Detection and Diagnosis of Power System

Conditions

Mathematical Morphology is a powerful nonlinear image and signal technique

that is based on set theory and lattice algebra. It was introduced by two French

researchers; Georges Matheron and Jean Serra in the mid-1960s to facilitate studies

of mineral deposits by characterizing physical properties of different minerals by

analyzing their geometrical structures [130, 131]. Since then, MM has found

applications in diverse arears such as; manufacturing system quality control,

computer graphics, medical imaging, etc. The nonlinear characteristics of MM

transforms have been used to process certain types of noise in images. These have

been applied in developing nonlinear filters where they are used in analysing the

geometrical shapes, surfaces and forms of objects.

Considering that power system signals can be viewed as one-dimensional

images whose information is contained in their graphs laid out in the Cartesian

coordinate or Euclidean space, then MM techniques can be used to extract such

information. Hence, in recent times, MM based tools and algorithms for power

system fault detection and diagnostics are being proposed considering MM’s ability

to extract features from any image or signal that are corrupted or embedded in white

noise [132],[133]. Moreover, MM is more attractive considering the computational

burden imposed by MM is lower than traditional techniques due to simple

mathematical operations such as additions and subtractions [134].


From the literature, it was found that, several MM based tools and techniques

have been proposed for power system condition monitoring and fault detection.

These include tools for monitoring and detection of power quality events [132],[133].

Other tools and methods for detecting power system disturbances were also proposed

by authors in [134],[135],[136],[137]. MM based tool for harmonic assessment was

proposed by [138] while several authors including those in [139],[140],[141]

proposed techniques for detection of CT saturation. Further MM based tools for

power transformer fault diagnosis were proposed by [142],[143]. MM based

techniques for transmission line protection were proposed by [144],[145],[146]

while authors in [147],[148] proposed MM based digital relaying.

Gautam and Brahma [78] proposed the CODO algorithm for HIF detection

where Open and Close MM filters are used to produce an output for extracting the

randomness feature of HIF. The CODO output, designated yCODO is created by taking

the arithmetic difference of the Close and Open MM operations using a three point

flat SE, g such that yCODO = (f ●g)(n) - (f ○g)(n). This algorithm generates spikes with

heights relative to the slope of the transients during faults. As previously mentioned,

the algorithm is limited to detecting the transients occurring only at or near the

positive and negative peaks. As the rate of appearance of transients depends on the

rate of change of the effective HIF impedance which is highly random, there is no

guarantee that transients will occur at those points on the input signal and hence

jeopardizing the robustness of the algorithm. Moreover, since the algorithm depends

on slope detection, this means presence of noise will cause spikes in the output of the

algorithm which can be easily confused with HIF. While a noise threshold of 15%

above the prefault value was used for HIF randomness feature extraction, no actual

noise conditions are simulated to test the CODO algorithm under such conditions.

Thus, it is obvious the application of mathematical morphology in power

systems condition monitoring as well as fault detection is becoming significant. In

this research, MM technique is utilised as the computational technique for

developing a fault detection and diagnostic tool based on time domain analysis fault

detection and diagnosis in both AC and DC power systems.

54 Chapter 3: Designing the Multistage MM Arc Fault Detection Algorithm

Chapter 3: Designing the Multistage MM

Arc Fault Detection Algorithm

3.1 INTRODUCTION

MM is a non-linear image/signal processing technique based on lattice algebra

and set theory that decomposes the geometrical features of the image or signal

waveform to extract hidden characteristics. MM is concerned with the shape of the

signal in complete time domain and is capable of extracting features from any image

or signal by means of a probing or filtering signal called the structuring element

(SE). The fault detection and diagnostic tool developed through this research is a

multistage Morphological filter, constructed from two nonlinear MM filters called

the morphological median filter (MMF) and the Alternating Sequential Filters (ASF)

called the decomposed open-closed alternating sequence (DOCAS) morphological

fault detector. The DOCAS algorithm is enhanced by an eccentrically decreasing

weighted SE that gives it the functional attributes to detect seemingly insignificant

changes in the topography of the signal waveform. This and other unique features of

the MM techniques are utilised by the DOCAS algorithm for application in feeder

OC protection, HIF feature extraction and classification in HIF detection as well as

DC arc-fault detection in PV systems.

This chapter is organised as follows: Section 3.2 discusses the research

methodology; section 3.3 discusses the mathematical fundamentals of the MM signal

processing technique; section 3.4 discusses the design of the fault detector algorithm

and its mathematical derivation and section 3.5 discuss the characteristics of the

algorithm and its application in the detection of power system disturbances. In

section 3.6 the conclusion is presented.

3.2 RESEARCH METHODOLOGY UTILIZING THE MM TECHNIQUE

The research methodology presented herein defines the overall design of the

research, and the way the research has been conducted to answer research questions

in achieving the research aims and objectives.

In designing the fault detection and diagnostic tool, various features and

functional attributes of the MM signal processing technique have been investigated

Chapter 3: Designing the Multistage MM Arc Fault Detection Algorithm 55

for their application to give the following characteristic to the fault detection and

diagnostic algorithm;

- Fault detection by means of sudden change in fault current magnitude using

displacement of the current magnitude from its prefault level.

- Integrate the functionality for suppression of the exponentially decaying

DC offset through the natural process of fault detection without parameter

estimation.

- HIF detection based on slope detection aiming two features of the HIFs

including, randomness and arc extinction and re-ignition

- DC arc-fault detection using slope detection (rate of change) of the chaotic

DC arc phenomena.

The functional attributes of the algorithm have been verified through

simulations of different fault conditions, including distribution feeder overcurrent

fault, high impedance fault and DC arc-faults on photovoltaic systems under low

irradiance levels.

The fault detection and diagnostic tool has been developed using the MATLAB

software. The simulation models, including High Impedance fault model and DC arc

model have also been implemented in MATLAB/Simulink. Moreover, all test beds

for the simulations including distribution feeder for overcurrent fault simulations as

well as the IEEE 13 bus test feeder and photovoltaic systems with interfacing

technology have been developed and implemented in MATLAB/Simulink.

3.3 BACKGROUND OF MM BASED TECHNIQUES

The MM method was primarily used in image processing; however, a class of

MM, known as Gray-scale morphology was developed for signal processing [149,

150] where morphological dilation and erosion are performed on the signal by a

structuring element(SE) which is also a signal by algebraic addition and subtraction

as opposed to union and intersection in the case of binary morphology . A signal can

be considered as a one-dimensional (1-D) image, whose information is contained in

its graph. The genesis of all MM filters is two primitive MM transforms called

“Dilation” and “Erosion”. In MM processing of analogue signals, gray-scale dilation

and erosion are applied. All other MM transforms and filters are derived from the

dilation and erosion transforms. The following propositions can be applied in


computing grayscale dilation and erosion. Let I{x1,x2, x3… xX} be the domain of the

signal i(x) where X is the total number of samples and G{p1,p2,p3…pP} be the domain

of the structuring element (SE) g(p) where P is the total number of points of the SE.

The SE can be a subset of i(x) such that P ≤ X, represented by points on the

Euclidean space or the Cartesian grid. The dilation and erosion transformation of the

signal i(x) by the structuring element g(p) are respectively defined in subsections

3.3.1 and 3.1.2.

3.3.1 Gray-Scale Dilation

The gray-scale dilation is obtained by algebraic addition defined by (3.1)

[149],[150]

( ) ( ) ( )

( )

( ) max

0 , 0

Gp

iDI i g x i x p g p

x p x p

= = − +

−

(3.1)

where iDIG is the new finite set formed by the dilation operation on the fault

signal i(x) by the SE g(p). The set iDIG contains the maximum selection of a set of

sums within the neighbourhood of the points around the origin of the SE g. The

process defined by (3.1) involves spatial translation of the signal i(x) by each point of

g(p) then offset by the value of g at point p [151]. Then the dilated signal iDIG is the

pointwise maximum selection of the translated and offset version of the original

signal i(x). The sequence of operation in the dilation transform can be demonstrated

by considering an illustrative example.

Supposing the signal i(x) to be dilated by a SE has 6 points represented on the

Cartesian coordinate with values such that i = [0, 0.1, 0.2, 0.3, 0.2, 0.1]. The SE g

has 3 points with origin at the center, such that the points are p = (-1, 0, 1) with

values of g at these points being g = [0.1, 0, 0.1]. Application of (3.1) involves a

pointwise maximum of three signals.

Let h(p) be a 1 by X matrix to hold the value of i(x) offset by g at each point p.

The result of the addition in the dilation operation on the signal i(x) by values of g at

each point p is held in the h(p) matrix as illustrated below.

( ) ( )1( ) ( 1) 1,.....,h i x g x X

− = + − =

( ) ( ) ( ) ( ) ( ) ( )

( 1)h 0 + 0.1 , 0.1 + 0.1 , 0.2+ 0.1 , 0.3 0.1 , 0.2 0.1 , 0.1 0.1

0.1, 0.2, 0.3, 0.4, 0.3, 0.2

− = + + +

=


( ) ( )0( ) (0) 1,.....,h i x g x X = + =

( ) ( ) ( ) ( ) ( ) ( )

(0) 0 + 0 , 0.1+ 0 , 0.2 + 0 0.3 0 0.2 0 0.1 0

0 0.1 0.2 0.3 0.2 0.1

h = + + +

=

( ) ( )1( ) (1) 1,.....,h i x g x X = + =

( ) ( ) ( ) ( ) ( ) ( )

(1) 0 + 0.1 , 0.1 + 0.1 , 0.2 + 0.1 , 0.3 0.1 , 0.2 0.1 , 0.1 0.1

0.1, 0.2, 0.3, 0.4, 0.3, 0.2

h = + + +

=

The results in the h(p) matrices can be placed in another matrix denoted as H. To

account for the effect of translation by each point of g(p), the signals h(-1), h(0) and h(1)

must be translated as shown in the H matrix. The negative infinity (-∞) is used in

dilation and meaning that the signal is undefined at that point.

1 1

0 1

1 1

H

X

X

X P X

h

h

h

−

− −

= − − − −

0.1 0.2 0.3 0.4 0.3 0.2

H 0 0.1 0.2 0.3 0.2 0.1

0.1 0.2 0.3 0.4 0.3 0.2

− −

= − − − −

The signal iDIG is formed by selecting the maximum value in each column in H as

shown below.

iDIG(1): max(H11, H21, H31) = max(0, -∞,-∞) = 0

iDIG(2): max(H12, H22, H32) = max(0.2, 0, -∞) = 0.2

iDIG(3): max(H13, H23, H33) = max(0.3, 0.1, 0.1) = 0.3

iDIG(4): max(H14, H24, H34) = max(0.4, 0.2, 0.2) = 0.4

iDIG(5): max(H15, H25, H35) = max(0.3, 0.3, 0.3) = 0.3

iDIG(6): max(H16, H26, H36) = max(0.2, 0.2, 0.4) = 0.4

iDIG(7): max(H17, H27, H37) = max(-∞, 0.1, 0.3) = 0.3

iDIG( 8): max(H18, H28, H38) = max(-∞, -∞, 0.2) = 0.2

0,0.2,0.3, 0.4, 0.3, 0.4, 0.3,0.2GiDI =


The domain of the dilated signal has been expanded due to the translation of the

signal i by –p for each point p on g in order to have the center of g under the signal i

[151]. Thus, any point on the domain of the SE that is not under the signal is

undefined. Therefore, the first and the last elements of the matrix iDIG are undefined

and denoted not applicable (NA).

,0.2,0.3, 0.4, 0.3, 0.4, 0.3,GiDI NA NA=

3.3.2 Gray-scale Erosion

Similarly, the gray-scale erosion of the fault signal i(x) by the g(p) is obtained

by algebraic subtraction defined in (3.2) [149],[150]

( ) ( ) ( ) ( ) min

0 ( ) , 0

Gp

iER i g x i x p g p

x p x p

= = + −

+

(3.2)

where iERG is the new finite set formed by the erosion transform using the

minimum selection of a set of differences within the neighbourhood of the points

around the origin of the structuring element, g. The sequence of operation in the

erosion transform can be demonstrated by considering an illustrative example.

The operation of the erosion transform can be demonstrated by considering the

same illustrative example previously used. The signal i(x) used previously is to be

eroded by the same SE, g. Application of (3.2) involves a pointwise minimum of

three signals.

The h(p) matrices and their contents are defined for the erosion process herein.

The result of the subtraction in the erosion operation on the signal i(x) by values of g

at each point p is held in the h(p) matrix as illustrated below.

( ) ( )1( ) ( 1) 1,.....,h i x g x X

− = − − =

( ) ( ) ( ) ( ) ( ) ( )

( 1)h 0 - 0.1 , 0.1 - 0.1 , 0.2- 0.1 , 0.3 0.1 , 0.2 0.1 , 0.1 0.1

-0.1, 0, 0.1, 0.2, 0.1, 0

− = − − −

=

( ) ( )0( ) (0) 1,.....,h i x g x X = − =

( ) ( ) ( ) ( ) ( ) ( )

(0) 0 - 0 , 0.1- 0 , 0.2 - 0 , 0.3 0 , 0.2 0 , 0.1 0

0 0.1 0.2 0.3 0.2 0.1

h = − − −

=

( ) ( )1( ) (1) 1,.....,h i x g x X = − =


( ) ( ) ( ) ( ) ( ) ( )

(1)h 0 - 0.1 , 0.1 - 0.1 , 0.2 - 0.1 , 0.3 0.1 , 0.2 0.1 , 0.1 0.1

-0.1, 0, 0.1, 0.2, 0.1, 0

= − − −

=

The results in the h(p) matrices can be placed in H as previously defined. To account

for the effect of translation by each point of g(p), the signals h(-1), h(0) and h(1) must be

translated as shown in the H matrix. The positive infinity (∞) is used in erosion to

represent points where signal is undefined.

1 1

0 1

1 1

H

X

X

X P X

h

h

h

−

=

-0.1 0 0.1 0.2 0.1 0

H 0 0.1 0.2 0.3 0.2 0.1

-0.1 0 0.1 0.2 0.1 0

=

The eroded value of i(x) results from the minimum selection of each column in the H

matrix. Let iERG be a 1 by X finite set containing the minimum selection of columns

of H. The minimum selection of the valid columns of the H matrix is shown below.

iERG(1): min(H11, H21, H31) = min(-0.1, ∞,∞) = -0.1

iERG(2): min(H12, H22, H32) = min(0, 0, ∞) = 0

iERG(3): min(H13, H23, H33) = min(0.1, 0.1, -0.1) = -0.1

iERG(4): min(H14, H24, H34) = min(0.2, 0.2, 0) = 0

iERG(5): min(H15, H25, H35) = min(0.1, 0.3, 0.1) = 0.1

iERG(6): min(H16, H26, H36) = min(0, 0.2, 0.2) = 0

iERG(7): min(H17, H27, H37) = min(∞, 0.1, 0.1) = 0.1

iERG( 8): min(H18, H28, H38) = min(∞, ∞, 0) = 0

0.1,0, 0.1,0,0.1,0,0.1,0GiER = − −

The reclassified iERG considering the effect of translating the SE is as given below.

,0, 0.1,0,0.1,0,0.1,GiER NA NA= −


3.3.3 Physical Effect of Dilation and Erosion Transforms

Dilation causes each point on the signal i(x) to increase in size while erosion

causes each point to shrink [132]. The effect of dilation transform is such that it

causes each point on the signal under consideration to grow (dilate) in size thereby

potentially filling the hole in dents on the graph of the signal. This characteristic is

very useful in noise removal and enhancement of the signal for the detection of any

abnormalities. The erosion operation is achieved by pointwise operation on the input

signal by the SE and the minimum of the difference of the local region of the signal

to which the SE operates on is taken. The effect of the erosion operation results in the

shrinking of the points to which the SE operates. The physical effects of the Dilation

and Erosion operations can be observed by a graphical demonstration on the example

set plot generated using MATLAB given in Figure 3.1(a) and (b) respectively.

Figure 3.1: Physical effect of (a) dilation and (b) erosion

3.3.4 Opening and Closing Transforms

The dilation and erosion are dual transforms. However, despite their duality,

the action of one transform cannot be recovered by directly applying the other

transform. That is, result obtained by first eroding a signal then dilating it will not be

the same as dilating it first then eroding it. The order in which the dilation and

erosion are applied in combination constitutes two new MM transforms, namely the

open and the close transforms. The open and close transforms are symbolised by ○

and ● symbolic operators respectively. Thus, the gray-scale opening and closing of

the signal i by SE g, denoted (i ○g) and (i ●g) respectively are defined by equations

(3.3) and (3.4), [152]

( ) ( ) ( ) ( )( ) max min ( ) ( )pp

i g i g g i x p g p x p g p= = + − − + (3.3)


( ) ( ) ( ) ( )( ) min max ( ) ( )p p

i g i g g i x p g p x p g p• = = − + + − (3.4)

As can be seen in (3.3), opening transform is the dilation of the erosion of the

signal i by the same SE g, and in (3.4), the closing transform is the erosion of the

dilation of the signal i by the structuring element g. The opening and closing

transforms are also dual transforms, where the opening action results in the reduction

of small positive regions within the signal, whilst the closing action will result in the

reduction of small negative regions within the signal. Furthermore, the opening and

closing transforms allow for the recovery of features of the signal lost due to the

erosion and dilation operations respectively, thus providing the characteristics

necessary for feature extraction [133].

The physical effect of opening and closing transforms are graphically

illustrated in Figures 3.2(a) and (b) respectively by considering the same fault current

i(x) and the structuring element g(p). The opening and closing transforms provide the

building block for the construction of two fundamental nonlinear morphological

filters called, Closing-opening and Opening-closing filters. These filters when

connected in cascade and operated simultaneously with the same SE can be very

effective in eliminating positive and negative impulse noise in the signal. These

properties of mathematical morphological transforms and filters can be exploited by

uniquely combining them to develop morphological fault detection and identification

algorithm which is fast, robust and reliable.

Figure 3.2: Physical effect of (a) opening and (b) closing transforms

3.3.5 Morphological Filters

Different classes and types of filters, depending on the application and

requirements can be derived through strategic combination of the basic MM

transforms. Two types of MM filters utilised in the design of the proposed power

system fault detection and classification algorithm are the morphological median


filter (MMF) and the alternative sequential filter (ASF). Thus, discussion of these

filters including mathematical description and graphical illustration of the signal

transformation by these filters is presented here.

Morphological Median Filters: The MMF is an averaging filter, where a new

intermediate finite average set can be achieved by taking the average of simultaneous

dilation and erosion of the target signal by the same SE. Considering the signal i(x)

and its dilation and erosion by the structuring element, g(p), as defined in (3.1) and

(3.2) respectively, and assuming the two primitive transforms are configured for

MMF operation, the average MMF output denoted AvMMF is given by (3.5) [150].

( )( ) ( )( )2 2

G GMMF

i g x i g xiDI iERAv

+ += = (3.5)

Alternating Sequential Filters: Alternating sequential filters (ASFs) in MM

are a combination of iterative morphological filters with increasing size of SEs. They

offer a hierarchical structure for extracting geometrical structure for extracting

characteristics of objects. The ASFs consist of iterative operations of opening and

closings with SEs of increasing sizes. Suppose g1(v) and g2(u) such that v > u are two

SEs. Consider the case of open-close ASF where the sequential transformation of the

signal i(x) by g1(v) and g2(u) is defined by (3.6) [149]

( )( )( )1 1 2 2ASFOC f g g g g= • • (3.6)

where OCASF is the output of the open-close alternating sequential filter.

Similarly, in the close-open ASF where the sequential transformation of the signal

i(x) by g1(v) and g2(u) is defined by (3.7) [149]

( )( )( )1 1 2 2ASFCO f g g g g= • • (3.7)

where COASF is the output of the close-open alternating sequential filter.

The ASFs offer a method to extracting image features hierarchically. The

features can be divided into different layers according to their corresponding SE

sizes. The features, such as distribution of each layer can be used in many

applications; for example, feature classification and recognition.


3.4 DESIGN OF THE MORPHOLOGICAL ALGORITHM FOR POWER

SYSTEM FAULT DETECTION

The fault detection and classification technique presented in this thesis proposes

to use voltage and current signals measured at the distribution feeder substation. The

measured voltage and current signals provide inputs to the HIF detection and

classification algorithm centrally located at the substation. The occurrence/existence

of faults is detected by continuously monitoring and analysing the extracted voltage

and current signals for changes in current and/or voltage magnitude.

In the proposed algorithm, two MM filters, namely MMF and ASF are

strategically combined, and with the use of the designed SE, create a MFD signal

output from the input fault signal quantities for fault detection. The mathematical

derivations for the entire signal processing technique in creating the MFD output is

described herein.

3.4.1 Input Signal

The input signals to the DOCAS algorithm are the fault current and voltage

signals measured at the distribution feeder substation. These signals can be

represented by a generalised sinusoidal waveform with exponentially decaying DC-

component given by (3.8);

( ) −++=−

tFeFtf

t

sin)( 10 (3.8)

where Foe-t/τ and F1sin(ωt + α – θ) are the DC and AC components respectively.

3.4.2 Sampling

A sampling frequency of 1.92 kHz suitable for real-time application [78] in the

detection of power system disturbances is applied in sampling the signal. It has been

further suggested that two times recommended sampling frequency could be used.

Thus, considering 64 samples per fundamental cycle in the design, a sampling

frequency of fs= 3200 Hz for 50 Hz system is used. All designed parameters

presented herein and thereafter will refer to 50 Hz system; however, values can be

recalculated by interchanging the frequency for 60 Hz system. Considering the

number of samples, and using (3.9) [153] , the sampling rate used in the design is

312.5 µs.


sN

TT 0= (3.9)

where ΔT is the sampling rate, T0 = 1/f0 = 1/50 = 0.2s is the period of the

fundamental cycle, and Ns = 64 is the number of samples in a fundamental cycle. A

sampling function consisting of string impulses defined as δ(t - nΔT) 6 n=-∞,…,+∞

being the number of samples is used to sample the fault signal in (3.8). The sampled

fault signal waveform can be seen as a time function, consisting of uniformly spaced

impulses, each with a magnitude f(nΔT) as given in (3.10).

( ) ( )TntTnftfn

−=

−=

)(' (3.10)

The signal can be written as sampled signal for a fundamental cycle as given in

(3.11);

( ) 0 1 sin 0,....., 1nf n F r F n n NN

= + + − = −

(3.11)

where r = e-(∆T/τ), and the first and second terms are the DC and the AC components

respectively [153]

( ) ( ) )()()()(1

0

TwtTwftwttftfW

w

w −== −

=

(3.12)

where fw(t) is the fault signal window. The wth sample in the data window can be

represented (3.13);

( ) −++= TkFeFkf Tk

w sin)( 10 (3.13)

The first and the last sample in the data window are at position k = 0 and k = N-1.

These samples define the beginning and the end of the data window. Each data

window is separated by ∆T. The data window can be written as in (3.14);

( ) 0 1 sin 0,....., 1nf n F r F n n NN

= + + − = −

(3.14)

Therefore, let F{n1 n2 n3…….nN} represent a window in the domain of the fault signal

f(n) to be processed by the MFD algorithm at each natural time step.

3.4.3 Design of Structuring Element

The design and/or selection of an optimum SE is fundamental to the MM

algorithm in enhancing its effectiveness in extracting fault signal characteristics


necessary for the secure and reliable detection and classification of power system

faults. Observing all the MMF equations from (3.1) to (3.7) it is obvious that the SE

is very crucial to the transformation of the target fault signal in achieving the desired

outcome. There are several factors, including the nature of signal under

consideration, its harmonic content and rate of sampling used in processing the signal

that influence the design and structure of the SE [132],[134].

While no specific guideline exists for selection of SE, it is noted that SEs of

different geometrical shape such as line, square, disk or ball-shaped and others

including bee line, curve, triangle have been used in different applications [150]. In

fault power system fault detection, the power systems signals (voltage and current)

are sinusoidal, and one-dimensional, thus the popular choice of SE has been the flat

linear structure. Flat SEs with different lengths and constant values have been

applied in different MM based filters proposed by various researchers including

those in [134],[142],[143],[145],[146],[154],[155],[156],[152] for the detection and

analysis of various power systems conditions and faults. However, Shih [149]

implies that, the use of flat SE in some situation may produce ambiguous results.

While it is observed that no specific guideline exists for the selection of appropriate

geometry and size of the SE, Gautam and Brahma [157] have done detailed analysis

for the selection of optimal SE. Based on this guideline, and in view of [149],

general structure of the SE used in the DOCAS algorithm that captures the variation

in faults signal inputs is defined by (3.15) [150];

mmmmmg cos)1cos(.....cos1cos....)1cos(cos)( −−= (3.15)

where m is the number of points in the SE; ϕ is the phase angle (ϕ = 2ᴫf0∆T); f0

is fundamental frequency and ∆T is the sampling interval. Note that, points of the

grayscale SE denoted p in (3.1) and (3.2) and all MM operation thereafter is replaced

by m in (3.15), and the reason will become obvious in the proceeding section.

Decomposition of Structuring Element: While the general SE as given in

(3.15) could be used in its entirety, it is not computationally efficient to do so as the

same result can still be archived with a smaller SE with less number of points. In the

overall design of the algorithm, a sampling frequency of 3.2 kHz to get 64 samples at

a sampling rate of ∆T = 312.5μs per fundamental in 50 Hz was used. Considering

that the 64 samples are segmented into data windows of 16 samples, the SE can have

points (or size) up to 16. However, more sampling points will increase computational


time resulting in increased fault delay. Moreover, the basic rule is that, the SE can be

a subset of the signal under consideration such that n > m, where n and m are number

of points on the signal and the SE respectively. Thus, considering the sampling rates

used, and in the interest of maintaining computational efficiency, a SE of up to 5

elements is adequate for real-time detection of power system faults. Thus, a 5 point

SE can be derived form (3.15) using m = (2n + 1) n =0,1,2 [150], such that m = 1

being the center point. Points m = 3 and m = 5 are located on both sides resulting in

an eccentrically decreasing convex geometrical shape with unequal slopes.

cos3 cos 1 cos cos3g = (3.16)

The designed sampling interval and fundamental frequency results in a

weighted eccentrically decreasing convex SE with points, g = [0.957, 0.995, 1,

0.995, 0.957]. Considering the geometrical shape of the SE in (3.16), there are two

unequal line segments with unequal slopes as shown in Figure.3.3. Further

enhancement to the SE is done by decomposition of the convex structure [158]. The

SE is decomposed, into two linearly sloped SEs with 3 elements defined as A1 =

[cos1ϕ, 1, cos1ϕ] and A2 = [cos3ϕ, 1, cos3ϕ] and having slopes s1 = 1 – cos1ϕ

and s2 = cos1ϕ – cos3ϕ, respectively such that s2 < s1. A1 and A2 are used in MMF

stages of the proposed algorithm, and must be sequentially applied such that A1 is

dilated and eroded by A2 to cover the length of the SE [149] . Further two SEs

designated B1 and B2, where B2 > B1 are composed with n = 2 for B2 = [cos3ϕ,

cos1ϕ, 1 cos1ϕ, cos3ϕ] and n = 1 for B1 = [cos1ϕ, 1, cos1ϕ] and used in the ASF

layers.

Figure 3.3: Eccentrically decreasing convex structuring element.


3.4.4 Weighted Morphological Signal Transformation

The geometry of the SE and its decomposition allows for weighted MM

transformation. Weighted morphological dilation and erosion of an input signal f(n)

by a weighted structuring element g(m) are defined by (3.17) and (3.18) respectively

[150].

( ) ( ) ( ) ( ) max /wm

DI f g m f n m g m= = − (3.17)

( ) ( ) ( ) ( ) min /wm

ER f g m f n m g m= = + (3.18)

Weighted morphological transform is implied for all MM operations applied

hereafter.

3.4.5 Processing the Morphological Fault Detector Signal

The mathematical derivations for the entire signal processing technique in

creating the MFD output is described herein. The sampled version of the fault signal

in (2) is processed as a sliding window with each window separated by ΔT. The fault

signal is processed by the respective filters in the sequence described and in

conjunction with the SEs defined.

Decomposed Morphological Median Filter: The decomposition of the SE into

A1 and A2 gives rise to two stages of MMF in the DOCAS algorithm called MMF-

Stage 1 and MMF-Stage 2 where A1 and A2 used respectively; such that the output of

stage 1 is cascaded to stage 2. To achieve the total dilation and erosion operations as

would have been over the range of the overall SE, the signal to be transformed must

be sequentially dilated and eroded by the decomposed elements [149]. The output of

the previous operation by the first component, A1 must be cascaded to the next

component, A2 giving the desired morphological transformation more efficiently.

The mathematical derivations in the signal transformation at the MMF stages

are described in herein.

MMF-Stage 1: The dilation and erosion of original fault signal for a data

window segment, f(n) by A1 are given by (3.19) and (3.20) respectively.

( ) ( ) 1 1 1, 1,max / 1,2,3m mm

DI f A f n a a m= = − = (3.19)

( ) ( ) 1 1 1, 1,min / 1,2,3m mm

ER f A f n a a m= = + = (3.20)


where DI1 and ER1 are defined as intermediate sets created by the dilation and

erosion process in MMF-Stage 1, and these are cascaded to the MMF-Stage 2 where

they are simultaneously dilated and eroded to create the final sets denoted DI2 and

ER2 respectively, and defined by (3.21) and (3.22)

MMF-Stage 2: The outputs of MMF-Stage 1 are dilated and eroded by A2 in

Stage 2 as defined by (3.21) and (3.22) respectively.

( ) ( ) 3,2,1/max ,2,21212 =−== maanDIAAfDI mmm

(3.21)

( ) ( ) 3,2,1/min ,1,21212 =+== maanERAAfER mmm

(3.22)

where DI1 and DI2 contain maximum selections of sets of sums from dilation

operation while ER1 and ER2 contain the minimum selections of set of differences

from erosion operation within the neighbourhood A1 and A2 in stage 1 and 2 of the

decomposed MMF respectively. The processed signal can be reconstructed by taking

the average output of the decomposed MMF as defined by (3.23);

++

+=

222

1 2211 ERDIERDIAvMMF (3.23)

where (DI1 + ER1)/2 and (DI2 + ER2)/2 are the average outputs of MMF-Stage 1 and

MMF-Stage 2 respectively. Equation (3.23) represents the magnitude transformation

of the original fault signal. Let fmmf (n) denote the transformed version of f(n) given

by (3.24);

( ) 0 1sin 0,....., 1

n

mmf mmf mmff n F r F n n N

N

= + + − = −

(3.24)

where F0mmf and F1mmf are the dc and ac components of the MMF average output

respectively.

The DOCAS algorithm is based on slope detection to generate spikes in

response to variation in the topography of the fault signal waveform caused by the

fault. The distortion of the signal waveform from the transients following a fault are

emphasised by subtracting fmmf (n), (3.24) from the original fault input signal, f(n)

(3.16) to produce the initial fault detection signal given in (3.25);

++=

−

=

nN

FrFnfN

n

n sin)(1

1

10 (3.25)


Alternating Sequential Filters: The ASF subsystem has two layers, the Open-

close ASF layer and the Close-open ASF layer, considered as Layer 1 and Layer 2

respectively. Each layer of the ASF subsystem has four stages whose outputs are

denoted OCs and COs where s defines a stage such that s∀=1,..,4. The cascaded

signal is simultaneously transformed by the same SE each stage of the ASF layers as

described in the subsequent subsections. The SEs used at the ASF layers are B1(u) ∀

u=1,2,3 and B2(υ) ∀ υ= 1,2,3,4,5 such that B2 > B1.

Open-close Alternating Sequential Filter: The four stages of the Open-close

ASF layers are denoted OCASF-Stage 1,..,OCASF-Stage 4 with outputs respectively

defined as OC1..OC4. The output of the upstream stage is cascaded to the

downstream stage where the outputs of the upstream and downstream stages can be

respectively defined as OCs and OCs+1 where 𝑠=1,.3. The mathematical derivation of

the Open-close ASF layer is described herein.

( )( )( )( )2 2 1 1( ( ) ( ) 1,...,5 and 1,.,3

ASFOC f B B B u B u = • • = =

(3.26)

where OCASF is the entire sequential transformation covering the four stages. A stage

is defined by a closing (●) and (○) operator in each ASF layer. Thus, each stage of

the Open-close ASF layer can be elaborated as follows;

OCASF-Stage 1:

( )

( ) ( )( )

1 2

2 2

( ) ( )

max min / ( ) / ( ) 1,..,5

OC n f B

f n B n B

=

= + − =

(3.27)

OCASF-Stage 2:

( )( )

( ) ( )( )

2 2 2

1 2 2

( ) ( )

min max / ( ) / ( ) 1,..,5

OC n f B B

OC n B n B

= •

= − + =

(3.28)

OCASF-Stage 3:

( )( )( )

( ) ( )( )

3 2 2 1

2 1 1

( ) ( ) ( )

max min / ( ) / ( ) 1,.,3

OC n f B B B u

OC n u B u n u B u u

= •

= + − =

(3.29)

OCASF-Stage 4:


( )( )( )( )

( ) ( )( )

4 2 2 1 1

3 1 1

( ) ( ) ( )

min max / ( ) / ( ) 1,.,3

OC n f B B B B u

OC n u B u n u B u u

= • •

= − + =

(3.30)

Close-open Alternating Sequential Filter: Similarly, the four stages of the

Close-open ASF layers are denoted COASF-Stage 1,..,COASF-Stage 4 with outputs

respectively defined as CO1..CO4. The output of the upstream stage is cascaded to

the downstream stage where the outputs of the upstream and downstream stages can

be respectively defined as COs and COs+1 where 𝑠=1,.3. The mathematical derivation

of the Close-open ASF layer is described herein.

( )( )( )( )2 2 1 1( ( ) ( ) 1,...,5 and 1,.,3

ASFCO f B B B u B u = • • = =

(3.31)

where COASF is the entire sequential transformation covering the four stages. A

stage has been previously defined is denoted by a closing (●) and (○) operator. The

signal transformation at each stage is thus described as follows;

COASF-Stage 1:

( )

( ) ( )( )

1 2

2 2

( ) ( )

min max / ( ) / ( ) 1,..,5

CO n f B

f n B n B

= •

= − + =

(3.32)

COASF-Stage 2:

( )( )

( ) ( )( )

2 2 2

1 2 2

( ) ( )

max min / ( ) / ( ) 1,..,5

CO n f B B

CO n B n B

= •

= + − =

(3.33)

COASF-Stage 3:

( )( )( )

( ) ( )( )

3 2 2 1

2 1 1

( ) ( ) ( )

min max / ( ) / ( ) 1,.,3

CO n f B B B u

CO n u B u n u B u u

= • •

= − + =

(3.34)

COASF-Stage 4:

( )( )( )( )

( ) ( )( )

4 2 2 1 1

3 1 1

( ) ( ) ( )

max min / ( ) / ( ) 1,.,3

CO n f B B B B u

CO n u B u n u B u u

= • •

= + − =

(3.35)


Morphological Fault Detector Output Signal: The MFD output signal is the

sum of difference between the sequential close and open transformations at each

layer of the ASF. Each layer has two sequential close and open transformations.

Let’s denote MFD1 as the difference between the close operation in COASF-Stage 1

and open operation OCASF-Stage 1; such that MFD1 = (CO1 - OC1). Similarly, MFD2

= (OC2 - CO2), MFD3 = (CO3 - OC3), and MFD4 = (OC4 - CO4). The MFD output is

then defined by (3.36);

4

1

s

s

MFD MFD=

= (3.36)

3.5 ATTRIBUTES OF THE DOCAS ALGORITHM

The entire signal transformation process to create the MFD output can be

graphically illustrated by considering a simple AC signal. Moreover, the attributes of

the MFD output and its application fault detection and classification are presented

herein.

3.5.1 DOCAS Response to Simple AC Signal

A pure sinusoidal signal at 50Hz fundamental frequency, of which the

normalized version shown in Figure 3.4 (a) was sampled at ∆T = 312.5μs and

segmented into data windows of 16 samples were passed through the DOCAS

algorithm. Domain of a signal data window is defined as fw= [k0, k1,…,kN-1] ∀ k =

0,…N-1 ,where k is a sample. Figure 3.4 (b) shows the average out of the

decomposed MMF stages. The edges of the windows are visible in the average MMF

output which shows the edge detection characteristic of the MMF. The edges are

fundamental to the detection of changes in the magnitude of the measured voltage

and/or current quantity, and they are emphasized by subtracting the average MMF

output from the original AC signal as shown in Figure 3.4 (c). The graph of Figure

3.4 (c) is the initial fault detection signal, ∆f (delta f). The initial fault detection

signal is created through the process described by (3.17) – (3.21) at the decomposed

MMF stages.

The technique applied in this algorithm makes use of one unique characteristic

of the MM in edge detection [159], [160]. This can be observed on the average

MMF output of Figure 3.4 (b), the leading and the lagging edges of the data window


are emphasised. The subtraction of the original signal and the average MMF output

further emphasises the edges as shown in Figure 3.4 (c). The edges are fundamental

in the application of the algorithm in detecting power system disturbances.

Figure 3.4: DOCAS Response at the MMF stages (a) Simple AC input signal, (b) MMF average

output and (c) Difference fault signal, ∆f

3.5.2 DOCAS MFD Fault Detection Windows

The passage of the initial fault detection signal, Δf through the DOCAS

algorithm at the ASF stages, and the extraction and combination of signal at each

stage of the ASF layers results in the MFD signal output of Figure 3.5.

The MFD output signal is created through the process described by (3.25) –

(3.34). In Figure 3.5, the MFD output signal is segmented into fault detection

windows accomplished through the edge detection characteristic of the MMF. The

process of summing the differences of closing and opening transformation at each

ASF stages results in the suppression of smaller spikes within the fault window and

giving visibility to the edge spikes. The MFD fault windows are fundamental to the

fault detection functionality of the DOCAS algorithm. Based on the design

consideration of 64 samples per fundamental period and data window size of 16

samples, a fault detection window is of a quarter cycle duration resulting four fault

detection windows per fundamental period. A fault detection window is defined by

two edge spikes, one beginning and one ending. Moreover, it can be observed that


one edge spike is taller than the other, and the order of appearance depends on where

the first sample was taken. The DOCAS algorithm generates spikes based on slope

detection. Naturally, steeper slopes occur on transition from high value region (near

the positive/negative peaks) to lower value region (zero-crossing) of the signal

waveform as compared to slopes at or near the positive/negative peaks. Thus, the

taller edge spikes occur towards the zero-crossing while the shorter edge spikes

occur towards positive and negative peaks. In a fundamental cycle, there are four tall

edge spikes and four shorter edge spikes. The MFD values for both the tall and short

edge spikes, represented by their heights are relative to the instantaneous peak value

of the AC signal input. Referring to Figure 3.5, it can be observed that two

consecutive tall edge and short edge spikes appear together. These spikes are

separated by ∆T = 312.5μs and represent the end and the beginning of two

consecutive fault detection windows. Let’s denote the tall edge and short edge spikes

as MFDTall and MFDShort respectively then their sequence of appearance can be

defined by (3.37) and (3.38), where k represents a point in the domain of the sampled

signal.

( )1,..,0

12

2−=

+

= k

TNk

TkNMFDTall (3.37)

( )=

+

= ,..,1

1k

TNk

TkNMFDShort (3.38)

Such that MFDTall(1) = 2kN∆T and MFDTall(2) = 2k(N+1)∆T are two

consecutive tall edge spikes and MFDShort(1) = kN∆T and MFDShort(2) = k(N+1)∆T

are the two consecutive short edge spikes. Note that, the MFD output depicted in

Figure 3.5 corresponds to the simple AC signal of Figure 3.4 (a) where it can be

noticed that all tall edge spikes are of equal MFD value of about 0.0775 while the

short edge spikes are of equal MFD value of 0.0475 both relative to the normalized

peak amplitude of 1 in the simple AC signal. Moreover, the MFD values of the tall

and short edge spikes quoted are not fixed values, however they correspond to the

signal considered and are used for illustration. The change in the MFD value

visualized by the change in the height of both the tall edge spikes and the short edge

spikes indicate occurrence of a disturbance.


3.5.3 DOCAS MFD Fault Detection Windows Time Delays

Supposing the beginning and ending of a fault detection window are

represented by MFDTall(1) and MFDShort(1) which separated by a time delay defined

as Twin = (N – 1)∆T. Based on the data window size of N = 16 and ∆T = 312.5μs,

Twin translates to 4.6875 ms. If we denote MFDTall(1)p being the tall edge spike in the

current fault window, and MFDTall(1)n being the tall edge spike in the next fault

window, then the time delay between these spikes can be defined as Tupdate = (2N-

1)∆T = 9.6875ms. The value of MFDTall is updated within this time delay for

continuous monitoring of the power system. The next time delay that needs to be

defined is Tdmax. This is the time delay in which a disturbance must be detected and

declared as a fault. In determining Tdmax, factors such as different fault inception

angle, power factor, severity of the fault and fault distance were considered.

Moreover, in considering that fault conditions are detected by edge spikes, the

minimum delay that would account for all factors considered was determined to be a

¾ cycles +1 sample. Hence, Tdmax = (3N+1)ΔT = 15.3125ms. This time can be

determined in terms of the tall window edge spikes as well. let tf = nfΔT nf = 0,.,N-

1denote the fault inception point, where nf is the faulted sample within a fault

window. The first point of fault level detection, denoted t1 is the beginning edge

spike of the next consecutive window such that, t1 = (N- nf + 1)ΔT nf = 0,…,N-1.

Since there are 5 edge spikes within Tdmax, the last point of detection is the beginning

edge spike of the 4th consecutive window after fault inception denoted t5 = (3N- nf +

1)ΔT nf = 0,…,N-1, such that Tdmax = t5.

From the foregoing, it can be deduced that, the minimum and maximum fault

detection delay imposed by the design are, ∆T and Twin respectively.

Figure 3.5: Fault detection windows of the DOCAS MFD output signal


3.5.4 Detecting Power System Disturbances

The DOCAS algorithm is designed to detect irregularities on the contour of the

graph of the input signal waveform caused by transients during power disturbances.

Moreover, any power system even usually results in swelling or decreasing of current

and/or voltage waveforms. These events are captured within the fault windows,

where appearance of any spikes within the window represents the deformities on the

graph of the input signal. The height of the spikes is relative to the rate of change in

the slope of the deformities on the contour of the graph. Furthermore, since the edge

spikes are within the data window set, the height of the edge spikes also increase or

decrease relative to the change in the instantaneous value of the input signal

waveform. Thus, variation in the heights of edge spikes during any disturbance is

relative to the variation in current and/or magnitude. While either or both edge spikes

can be monitored for occurrence of any disturbance, the DOCAS algorithm considers

only the tall edge spikes. The change in height of the tall edge spikes in the MFD

outputs of the measured quantities (voltage and current) indicates the presence or

occurrence of an event. This can be demonstrated by considering a typical single to

ground fault. Figures 3.6 and 3.7 show the response of the DOCAS algorithm to

current and voltage forms respectively for a SLG fault on phase A at 0.03s.

Figures 3.6 (a) and 3.7(a) show the faulted phase (phase A) current and voltage

waveforms respectively, where there is an increase fault current magnitude with a

corresponding decrease in voltage magnitude. The respective MFD output signal for

the fault current and voltage for each phase are shown in Figures 3.6 (b)-(d) and

3.7(b)-(d), which obviously show relative changes in the MFD edge spikes with

respect to the corresponding phase current and voltage. The MFD outputs of the

unfaulted phases do not show significant variations, if any at all. The MFD output of

the faulted phase (phase A) on the other hand shows significant variation in the MFD

edge spikes. Hence, power system disturbances can be detected by monitoring the

variation in MFD edge spikes. While both tall and short edge spikes can be used, we

will however use the tall edge spikes. The two consecutive tall edge spikes

corresponding to phase A fault current and voltage are shown in Figures 3.8 and 3.9

respectively. In overcurrent protection, the current magnitude is monitored, thus

variations in current magnitude is monitored in this context.


The tall window edge spikes, designated MFDTall are continuously monitored

for an indication of the occurrence of any disturbance. The disturbance is detected by

deviation in fault current magnitude from the prefault magnitude. Thus, let Imax(w) be

the maximum instantaneous current magnitude in the present fault detection window

represented by its MFDTall value, and Imax(w+1) be the maximum instantaneous

current magnitude in the previous window represented by its MFDTall value stored in

the memory. The increase in fault current magnitude is detected by computing the

increase in the MFDTall value from Imax(w+1) to Imax(w) as defined by (3.39).

( )( ) ( )

( )1

1

max

maxmax

+

+−=

wI

wIwIpuIinc (3.39)

where Iinc(pu) is the per unit increase in the current magnitude. Generally, in

overcurrent protection, increase in current magnitude is sensed for abnormalities or

short circuit fault conditions. Thus, if Iinc ≤ 1, no abnormality exists, or no

disturbance has occurred, and the system is in normal state. If Iinc > 1, then

disturbance has occurred. The detection of any disturbance does not necessarily

mean a fault exists. To possibility of a fault exist though and must be tested against

overcurrent threshold parameter to determine if the disturbance is a fault or a normal

operating condition.

3.5.5 Memory Update

The MFDTall value corresponding to Imax(w+1) stored in the memory buffer is

updated every 9.6875 ms with the MFDTall value corresponding to Imax(w) under

normal state. That is, if Iinc ≤ 1, update Imax(w+1) and continue monitoring. If Iinc

> 1, the value of Imax(w+1) before the disturbance is retained, and only updated

when the threat has been cleared.

By continuously updating the current signal MFD value in the memory, the

state of the power system is continuously monitored. Any change in the load

condition or network topology change affects the MFDTall value, and by performing

the check defined in (3.25), the state of the power system network is monitored at

regular interval of 9.6875 ms.


Figure 3.6: DOCAS response to SLG fault, (a) Fault current waveforms, SLG fault on phase

A, (b)-(d) corresponding MFD Outputs for each phase.

Figure 3.7: DOCAS response to SLG fault (a) Fault voltage waveforms for SLG fault on

phase A, (b)-(d) corresponding MFD outputs for each phase.

Figure 3.8: The MFD tall edge spikes for current, (a) MFDTall (1) and (b) MFDTall (2) The

MFD tall edge spikes for current, (a) MFDTall (1) and (b) MFDTall (2)


Figure 3.9: The MFD tall edge spikes for voltage, (a) MFDvTall (1) and (b) MFDvTall (2)

3.5.6 DOCAS Attributes for HIF Feature Extraction

The DOCAS algorithm operates on the fault signal inputs to extract features for

HIF detection and classification. The two HIF characteristics targeted by the DOCAS

algorithm are 1) arc extinction and re-ignition of the HIF arc around the fundamental

period resulting in shoulder shaped unsymmetrical fault current waveform and 2)

randomness, due to randomly changing effective fault resistance, Rf resulting in

erratic fault current waveform. The DOCAS algorithm generates MFD spikes at

specific locations on the output MFD signal that correlate to the target HIF

characteristics. The MFD fault detection windows are fundamental to this concept

where changes in the edge spikes as well as appearance of random spikes within the

fault detection window correlate to transients generated by a disturbance or an event.

The HIF current flowing through the high impedance path represented Rf

normally result is very low RMS current magnitude of between 10A-50A. While this

will not have any noticeable or significant impact on the fault current seen by the

feeder OC relay, it is however assumed that the HIF current will leave its footprint

on the feeder current and voltage signals. The DOCAS algorithm can detect

insignificant changes on the topography of the input signals and generates MFD

spikes at its output that correlate to these changes. While both voltage and current

signals can be considered for HIF feature extraction, in the DOCAS algorithm the

voltage signal is utilised as it was observed that voltage signal presented better

transient characteristics compared to current signal.

HIF Arc Extinction and Re-ignition Characteristic: The current and voltage

waveform intersect once every half cycle. If they are in phase, then this happens at

zero magnitude of each signal. If they are not in phase, then at the point of

intersection, the instantaneous voltage and current magnitude are equal. In a 50 Hz


system, this repeats every 0.01s. A spike is generated in the MFD output at the point

of intersection that correlates to this phenomenon as shown in Figure 3.10(a).

Let’s denote the spikes as MFDARC, and the point of intersection as Tθ, then the

MFDARC spikes can be defined by (3.40);

2 0,...,ArcMFD T kN T k= + = (3.40)

The AC arc extinguishes and re-ignites twice every have cycle. The arc

remains extinguished for a short period of time while waiting for the voltage to

regain until it reaches the restriking level to overcome the gap separation break down

voltage for arcing to happen again. This phenomenon impacts the fault voltage at

specific and fixed period defined in (3.40). The arc extinction and re-ignition thus

causes the MFD spikes defined as MFDArc to grow in height when HIF occurs.

Hence, the MFDArc spikes can be separated to observe the arc extinction and re-

ignition HIF characteristic. The MFDArc spikes maintain uniform height under

normal condition as observed in Figure 3.10(c) for the period before 0.03s. A HIF is

initiated at 0.03s and the HIF arc extinction and re-ignition occurs about zero-

crossing of the fundamental period shown in Figure 3.10(b) resulting in increased

height of the MFDARC spikes above the prefault level as shown in Figure 3.10(c) for

the period after 0.03s.

HIF Randomness Characteristic: The randomness characteristic would be

captured at the edge spikes and fault windows as previously defined (refer Figure

3.5). The tall and short edge spikes, MFDTall and MFDShort respectively defined in

(3.37) and (3.38) randomly vary in height under HIF condition. Transients occurring

closer to the zero-crossing of the fundamental cycle will cause random variation in

the height of MFDTall spikes while those closed to the positive and negative peaks

will cause random variation in the height of the MFDShort Spikes.

The appearance of the elongated edges spikes is non-uniform and random, and

can occur at any point from k = 0 to ∞-1 and k = 1,..,∞ as defined by (3.37) and

(3.38) if HIF exists. Those transients occurring away from the zero-crossing and the

positive and negative peaks will cause random spikes to grow inside the fault

windows. These spikes defined as MFDWindow will appear inside the fault windows

and will persist in the presence of HIF.


Figure 3.10: DOCAS output for HIF arc extinction and re-ignition feature (a), voltage

signal and current signals, (b) fault voltage and HIF current, and (c) MFDv output showing

target MFDArc spikes

3.5.7 DOCAS Response to DC Arc-Fault

The DOCAS algorithm is a multistage MM filter designed for the detection and

diagnosis of all types of faults in radial distribution feeders and DC arc-faults on the

DC bus of DC power systems including PV systems. The structure of the DOCAS

algorithm and its mathematical derivations are universally applied in all respect.

However, the input signals are different where its application in fault detection and

diagnosis in the radial distribution feeder relies on AC voltage and current signals as

input quantities while in DC arc-fault detection, the input signals are DC voltage and

current. Moreover, the SEs, A1, A2, B1 and B2 derived from the eccentrically

decreasing convex SE are utilized in the DOCAS algorithm in DC arc-fault

detection. While the SE used is designed from attributes of the AC signal, its reduced

structure for computational efficiency giving five points eccentrically decreasing

weighted convex having the following points [0.957, 0.995, 1, 0.995, 0.957] with

two unequal slopes is adopted without any change for DC arc-fault detection. The SE

as previously applied in AC system fault detection are decomposed into, A1 = [0.995,

1, 0.995], A2 = [0.957, 1, 0.957], B1 = [0.995, 1, 0.995] and B2 = [0.957, 0.995, 1,

0.995, 0.957]. A1 and A2 are used in the MMF section while B1 and B2 are used in the

ASF section.

The DOCAS algorithm as a DC Arc-Fault Detector retains its original design,

however its response to AC and DC signal inputs are significantly different. The


attributes of the DOCAS algorithm as a DC Arc-Fault Detector can be observed

through simulation of a DC arc-fault. DOCAS response to a DC arc-fault on the DC

side of a PV system for the DC current and voltage are shown in Figures 3.11 and

3.12 respectively.

DC Arc-Fault Detection: The DOCAS algorithm generates MFD spikes at its

output based on sloped detection. Figures 3.11(a) and 3.12(a) are the DC current and

voltage inputs to the DOCAS algorithm respectively where DC arc-fault occurs at

1.5s. The DC input signals are transformed by the decomposed MMF stages whose

average current and voltage outputs are given in Figures 3.11(b) and 3.12(b). The

average DC current and voltage outputs of the MMF stages are respectively

subtracted from the DC current and voltage input signals to produce the difference

DC current and voltage signals in Figures 3.11(c) and 3.12(c).

The difference DC current and voltage signals are transformed by the two layers

ASF, and at each stage of the ASF layers, the transformed signals are extracted and

combined to form the MFD outputs according to (3.36), and the results are shown in

Figures 3.11(d) and 3.12(d) for the DC current and voltage respectively.

It is obvious from Figures 3.11 and 3.12 that the concept of fault detection

window is not available because of the non-sinusoidal nature of the (DC) signal. The

onslaught of the arc is detected by appearance of spikes which increase in height in

response to the fast changing (rate of change of the random DC arc) noise like DC

arc characteristic that sustains when ignited. The spikes in the MFD output appear

chaotically. The DOCAS algorithm detects DC arc-fault by means of detecting the

chaotic behaviour of the random DC arc. Moreover, the MFD output only produces

spikes when arcing occurs.


Figure 3.11: DOCAS response to DC arc-fault in PV systems, (a) DC arc-fault voltage, (b)

Average MMF output, (c) diff DC fault voltage, ΔV and (c) MFD output.

Figure 3.12: DOCAS response to DC arc-fault in PV systems, (a) DC arc-fault current, (b)

Average MMF output, (c) diff DC fault current, ΔI and (c) MFD output.

3.6 CONCLUSION

A multistage fault detection and diagnostic tool called the DOCAS algorithm

utilising two classes of nonlinear morphological filters called the MMF and ASF was

developed. A SE with a sinusoidal geometrical structure was designed based on the

sampling rate and the fundamental frequency with points as many as 16 points

representing the data window. The SE was restructured in the interest of maintaining

computational efficiency to five points without compromising the integrity of the

algorithm. The restructured algorithm resulted in an eccentrically decreasing SE with

two unequal slopes. Further restructuring was made by decomposing the SE into two


separate SEs with equal slopes allowing for weighted dilation and erosion to enhance

the functionalities of the DOCAS algorithm in fault detection and diagnosis as well

as feature extraction. The algorithm’s functional attributes and characteristics for

fault detection including feature extraction for HIF detection and classification were

evaluated by analysing its response to a simple AC input. Furthermore, the algorithm

was subjected to a SLG fault to determine its behaviour, especially in detecting any

abnormal condition by detecting changes in current and/or voltage magnitudes.

Furthermore, the features of the DOCAS algorithm in responding to the simple AC

signal provided means to develop a technique for adaptive threshold parameter

setting in OC protection by periodically updating the MFD value in a memory buffer

in response to changes in the current magnitude due to changing load and network

changes.

The structure of the DOCAS algorithm and decomposition of the SE allowed

for its seamless application in DC arc-fault detection with any adjustment. In

evaluating the DOCAS algorithm as a DC Arc-Fault Detector, DC current and

voltage signals under DC arc-fault were used to analyse its behaviour. It was

observed that, DOCAS algorithm as a DC Arc-Fault Detector exhibited different

characteristics as opposed to its application in AC system fault detection. The

DOCAS MFD algorithm generates MFD spikes by detecting rate of change (slope

detection) rather than the displacement. Thus, DOCAS algorithm as a DC Arc-Fault

Detector detects DC arc-fault by detecting the chaotic behaviour of the DC arc-fault

by generating MFD spikes in response to the rapid change in the DC current and

voltage signals when subjected to sustained DC arc-fault.

Through the simulations the performance of the DOCAS algorithm as a tool for

fault detection and diagnosis in both AC and, specifically DC arc-fault detection in

DC systems have been verified. The features and functional attributes of the

algorithm showed characteristics for its application in; radial distribution feeder OC

protection with adaptive threshold parameter setting, feature extraction for HIF

detection and the detection and classification and DC arc-fault detection.

84 Chapter 4: Adaptive Overcurrent Protection in Active Radial Distribution Feeders with RE Based DERs

Chapter 4: Adaptive Overcurrent Protection

in Active Radial Distribution

Feeders with RE Based DERs

4.1 INTRODUCTION

The increased penetration of RE based DERs at the radial distribution feeders

transforms the feeders from being passive with unidirectional current flow to being

active networks bidirectional current flow. This phenomenon reduces the current

contribution by the grid due to current injection by the DERs which consequently

impacts on the feeder OC protection. This chapter begins by 1) presenting a

theoretical analysis based on Thevenin equivalent circuit modelling and

decomposition of the circuits into sequence networks to determine relationship for

the level of fault current injection under fault condition by the PV systems. Then 2),

a radial distribution feeder with several PV systems is modelled using Simscape

library objects in MATLAB/Simulink. Different fault conditions, including single

line-to-ground (SLG), line-to-line-to-ground (LLG) and three phase (3-Ph) faults

were simulated at different locations on the feeder length under different PV

penetration levels, and the results were analysed using the DOCAS algorithm to

verify trend in the fault current magnitude reduction due to PV system penetration.

Then a method for short-circuit fault detection with adaptive threshold parameter and

adaptive inverse time overcurrent (ITOC) relaying using the DOCAS (MFD) output

is proposed to overcome the challenges in feeder OC protection influenced by the

increasing RE based DER penetration such as PV systems. Moreover, the chapter

describes the theoretical analysis and presents the simulation results of the technique

used in suppressing the exponentially decaying offset.

The rest of the chapter is organised as follows; in sections 4.2, 4.3 and 4.4 a

theoretical method based on Thevenin equivalent circuit as well as distance factors of

PV and fault locations are presented. In sections 4.5 and 4.6 a secure and robust

method for adaptive OC protection in radial distribution feeders with PV penetration

using the DOCAS algorithm with adaptive OC threshold is presented. In section 4.7

the simulation results and discussion are provided on the impact of PV penetration on

Chapter 4: Adaptive Overcurrent Protection in Active Radial Distribution Feeders with RE Based DERs 85

feeder OC protection. In section 4.8, a method for adaptive inverse time OC relaying

using the DOCAS algorithm is presented followed by conclusion in section 4.9.

4.2 THEVENIN EQUIVALENT PARAMETER ESTIMATION

4.2.1 Photovoltaic Distributed Energy Resources

In Figure 4.1, a typical radial distribution feeder with PV system connection

is shown and provides the system for analysis in the proceeding sections.

In the system in Figure 4.1, all measurements for the analysis are assumed to

be taken from the feeder substation and the point of common coupling (PCC) of the

PV systems. Supposing there is U number of PV systems connected at various points

along the distribution feeder. The feeder voltage is nominally at 25kV, and all PV

sources are connected via Δ//Y transformers at PCC. Moreover, it is assumed that the

main feeder overcurrent protection relay is located at the main feeder substation, and

the voltage and current measurements are taken for fault detection. However, based

on recent level of technology integration and practice it is also possible to take

individual voltage and current readings by installing digital fault recorders (DFRs) at

the PCC of each PV system for dynamic estimation of Thevenin models of the PV

sources[161]. Furthermore, it is assumed that all measurements taken are

synchronized with the signal measurements taken at the feeder substation.

Figure 4.1: A typical radial distribution feeder with PV penetration

4.2.2 Thevenin Equivalent Voltage and Current

Assuming that a PV source could be coupled to the feeder at any point on the

feeder length, then the point at which a PV source is coupled to is defined as


𝑃𝑉(𝑢)∀𝑢 = 1. . , 𝑈. The voltage and current measured at the point of coupling of the

uth PV can be defined as 𝑉𝑃𝑉(𝑢) and 𝐼𝑃𝑉(𝑢) respectively. Considering that the PV

sources are assumed to inject only positive sequence voltage and current under load

condition, then the positive-sequence voltage and current measured at PV(u) can be

defined as 𝑉(1)𝑃𝑉(𝑢) and 𝐼(1)

𝑃𝑉(𝑢) respectively. Then an equation that relates the

per phase (positive-sequence) prefault voltage and current for the uth PV source can

be expressed in (4.1) [161]

(1) (1) (1)( ) ( ) ( ) ( )PV PV PVE u V u Z u I u= + (4.1)

where 𝑍𝑃𝑉(1)

(𝑢) is the series positive-sequence Thevenin equivalent impedance at

PV(u) for all interfacing devices including the transformer. Supposing a fault occurs

at some point on the feeder, the equation in (4.1) can be modified to reflect the fault

condition as given in (4.2);

(1) (1) (1)( ) ( ) ( ) ( )PV PVF PV FE u V u Z u I u= + (4.2)

where 𝑉𝐹𝑃𝑉

(1)(𝑢) and 𝐼𝐹𝑃𝑉

(1)(𝑢) are the positive-sequence fault voltage and current

respectively seen at PV(u) injected by the uth PV source.

4.2.3 Thevenin Sequence Impedances and Sequence Networks

The positive sequence Thevenin impedance at PV(u) can be determined by

equating (4.1) and (4.2). Denoting, ∆𝑉𝑃𝑉(1)(𝑢) = (𝑉𝑃𝑉

(1)− 𝑉𝐹𝑃𝑉

(1))(𝑢), is the change in

positive-sequence voltage magnitude from prefault condition to fault condition, and

similarly, ∆𝐼𝑃𝑉(1)(𝑢) = (𝐼𝐹𝑃𝑉

(1)−𝐼𝑃𝑉

(1))(𝑢) is the change in current magnitude from

prefault condition to fault condition, such that, ∆𝑉𝑃𝑉(1)(𝑢) = 𝑍𝑃𝑉

(1)∆𝐼𝑃𝑉

(1)(𝑢). Then the

positive-sequence Thevenin impedance at PV(u) can be determined by (4.3).

(1)(1)

(1)( ) ( )PV

PV

PV

VZ u u

I

=

(4.3)

The positive sequence voltage and current measured by the DFRs at the PCC

of the PV sources are phase values. The zero- and negative sequence impedance can

be obtained following the standard convention as indicated by Figure 4.2. The zero-


and negative-sequence impedances at PV(u) can be obtained by transforming the

voltage to sequence components as follows;

( ) ( )s pV u AV u= (4.4)

( ) ( )s pI u AI u= (4.5)

where Vs and Is are sequence voltage and current respectively, and similarly Vp and Ip

are phase voltage and current respectively. A is the transformation matrix such that,

2

2

1 1 1

1

1

A a a

a a

=

(4.6)

where 𝑎 = 𝑒𝑗2𝜋

3 . Then using standard notations of sequence networks shown in

Figure 4.2, the negative- and zero-sequence Thevenin impedances for the uth PV

source are;

(0)

(0)

(0)( ) ( )PV

PV

F

PV

F

VZ u u

I= − (4.7)

(2)

(2)

(2)( ) ( )PV

PV

F

PV

F

VZ u u

I= − (4.8)

All inertial sources including the substation source are modelled similarly using the

voltage and current measurements at their point of coupling. Replacing all subscripts

in (4.1) - (4.2) from PV to SS, the positive-, negative- and zero-sequence Thevenin

impedances of the substation (SS) source as seen from the point of measurement are;

𝑍𝑆𝑆(1)

, 𝑍𝑆𝑆(2)

and 𝑍𝑆𝑆(0)

respectively.

Figure 4.2: PV system sequence networks

The fault current contribution of PV(u) seen at the substation can be

determined by appropriate cascading of the relevant sequence networks, depending

E(m) 𝑉𝑃𝑉0

𝑍𝑃𝑉1 (𝑢)

𝐼𝑃𝑉1

𝑉𝑃𝑉1

Positive sequence


𝐼𝑃𝑉2

𝑉𝑃𝑉2

Negative sequence

𝐼𝑃𝑉0

Zero sequence



on the type of the fault at the point of common coupling with the sequence networks

of the distribution feeder.

4.3 EFFECT OF PV SYSTEM PENETRATION ON FEEDER SUBSTATION

FAULT CURRENT LEVEL

Considering the system in Figure 4.1, assume that there are U number of PV

systems connected at locations along the feeder length such that, the distance to the

point of coupling of the uth PV from the substation is denoted dPV(u). Supposing the

total feeder distance is l, then a distance factor, denoted df(u)∀ u=1,..,U can be

defined by (4.9).

( )( ) 1,...,f

dPV ud u u U

l= = (4.9)

PV1 is the nearest PV system to the substation with a distance of dPV(1). It is

assumed in this analysis, that PV1 is connected at a close proximity to the feeder

substation, thus is assumed to have a zero meter (dPV(1) ≈ 0 ) from the substation .

The furthest PV system is PV(U), where dPV(u) = l, then df can have possible values

between 0 and 1.

Supposing a fault occurs at some point on the distribution feeder of Figure 4.1.

Similarly, the distance to fault as seen from the substation can also be defined by a

fault distance factor denoted dff. Supposing the distance to fault is denoted dF in

meters; then the fault distance factor can be defined as;

ff

dFd

l= (4.12)

dff can have values between dffmin = 0 and dffmax = 1, where fault closes to the

substation, assuming at PV(1) will have dffmin value while a fault at the end of the

feeder, assuming at PV(U) will have dffmax value.

4.3.1 Fault Location with Respect to PV System Location

The following test conditions can be established to determine the location of

the fault with respect to PV(u). If dff = df(u), the point of fault is at PV(u). If dff >

df(u), the point of fault is on the right or downstream to PV (u) with respect to the

direction of current flow. On the other hand, if dff < df(u), then the fault is on the left

or upstream to PV(u) with respect to the direction of the current flow.

Alternatively, supposing ε(u) denotes a deviation between dff and df(u), such

that 𝜀(𝑢) = 𝑑𝑓𝑓 − 𝑑𝑓(𝑢) is a measure of proximity to the fault location with respect


to the uth PV system. The factor ε can have absolute value between 0 and 1, such that

| ε(u) | = 0 indicates fault location is at PV(u) and | ε(u) | = 1 indicates fault location

either at the feeder substation or at the end of the feeder length with respect to PV(U)

and PV(1) respectively. The smaller the | ε(u) | is, the closer the fault is to PV(u).

Fault can be located at either downstream (on the right) or upstream (on the left) of

PV(u) with respect to the direction of the current flow. The direction of the fault

location with respect to PV(u) depends on the polarity of ε(u). If ε(u) is a positive

value the fault is downstream, and if ε(u) is a negative value the fault is upstream.

4.3.2 PV Systems Fault Current Contribution

Supposing the radial feeder of Figure 4.1 has five PV systems (U = 5)

connected at points along the feeder length. A bolted three-phase fault occurs at

location between PV(3) and PV(4). The per phase diagram of the system under fault

is shown in Figure 4.3 with the point of fault designated F. All PV sources with dff >

df(u) and having ε(u) with positive values are categorized as downstream sources as

they feed fault current downstream to the fault. Conversely, all PV sources with dff <

df(u) and having ε(u) with negative values are categorized as upstream sources and

feed the fault that is upstream.

Let 𝐼𝐹1 and 𝐼𝐹2

respectively denote the total fault currents injected by all

downstream and all upstream sources respectively such that the total fault current at

point F is 𝐼𝐹 = 𝐼𝐹1+ 𝐼𝐹2

. Supposing there are D number of PVs feeding fault current

downstream to the point of fault F and (U-D) number of PVs feed fault current

upstream; then the fault current injection by these PV sources can be denoted 𝐼𝐹𝑃𝑉𝑑𝑛

and 𝐼𝐹𝑃𝑉𝑢𝑝 respectively, where;

( )

1PVdn PV u

D

F F

u

I I=

= (4.13)

( )PVup PV u

U

F F

u U D

I I= −

= (4.14)

The total fault current injected downstream to the point of fault is;

1 ( )

1ss PV u

D

F F F

u

I I I=

= + (4.15)


where IFss is the fault current contribution from the substation source which is

additive to the fault current injection by all downstream PV sources.

Assuming 𝐼𝐿 ≈ 0, the total fault current injected upstream to the point of fault

is;

2 ( )PV u

U

F F

u U D

I I= −

= (4.16)

The total fault current seen at the point fault is;

( ) ( )

1ss PV u PV u

D U

F F F F

u u U D

I I I I= = −

= + + (4.17)

Noting that 𝐼𝐹𝑆𝑆 is the fault current contribution by the substation source, it is

obvious from (4.17) that high penetration of PV systems will increase the fault

current contribution by them, and consequently reduce the fault current magnitude

seen at the feeder substation. Moreover, 𝐼𝐹𝑆𝑆 is a component of 𝐼𝐹1

which is the total

fault current injection from all sources, including the substation source feeding the

fault downstream. Hence, a large number of downstream sources with respect to the

fault location will also reduce the fault current level seen at the feeder substation.

This implies that, fault closest to the substation will register higher fault current

contribution from the substation source as there will be less downstream PV sources.

As the fault moves further away, the fault current contribution by the substation

source will reduce as the PV sources feed the fault. However, with high penetration

of PV DERs with large capacitor, the fault current magnitude seen at the feeder

substation can be significantly reduced irrespective of the fault location thus

affecting the over current system protection system coordination.

Figure 4.3: Per phase circuit diagram of the typical radial feeder system with PV


4.4 EFFECT OF FAULT LOCATION ON FEEDER SUBSTATION FAULT

CURRENT LEVEL

An equivalent per phase circuit diagram categorising the downstream and

upstream PV sources with respect to the point of fault F is shown in Figure 4.4., and

basing on this circuit arrangement, let 𝑉𝑃𝑉𝑑𝑛 and 𝑉𝑃𝑉𝑢𝑝 be the Thevenin equivalent

voltages of the downstream and upstream PV sources respectively. 𝑍𝑇ℎ𝑑𝑛 is the

Thevenin equivalent impedance of all the downstream PV sources and the

impedances of downstream feeder line segment between PV sources. Similarly,

𝑍𝑇ℎ𝑢𝑝 is the Thevenin equivalent impedance of all the upstream PV sources and the

impedances of feeder line segment between each upstream PV sources. As

previously considered, there are five PV sources (U = 5), and a short-circuit fault

occurs on the feeder between PV(3) and PV(4) such that PV(3) is the Dth PV source

and PV(4) is the (U-D)th PV source, then ZThdn′ and ZThup′ take the form in (4.18)

and (4.19) respectively.

' 1

1 1

1 (3)(3)

1 1

1 (2)(2)

(1)

Thdn

PVL

PVL

PV

Z

ZZ

ZZ

Z

=

+

+

+

+

(4.18)

' 1

1

1 1

1 (4)(5)

(5)

Thup

PVL

PV

Z

ZZ

Z

=

+

+

(4.19)

where ZPV(u) is the Thevenin impedance of the PV source seen at the PCC and

ZL(u) is the impedance of the line segments between PV sources along the feeder.

Supposing the impedance per meter of the feeder line can be represented by z Ω/m,

then the total feeder line impedance is Z = zl. The feeder line impedance between

PV(u) and the substation, in terms of the distance factor is then,

( ) ( )L fZ u d u Z= Ω (4.10)


Then, the total impedance of the feeder line in terms of the line segments from the

PV(1) to PV(U) can be expressed by (4.11),

( )2

( ) ( 1)U

L L

u

Z Z u Z u=

= − − Ω (4.11)

The impedance of the line segment between PV sources in terms of the distance

factor, df(u) is, ZL(u) = Z(df(u) – df(u – 1)). It must be noted that, the Thevenin

impedances defined in (4.18) and (4.19) do not include the impedance of the faulted

line segment. Considering that the fault occurs in the line segment between PV(3)

and PV(4), the impedance of the fault line segment is then ZL(4) such that in terms of

PV distance factors, ZL(4) = Z(df(4) – df(3)). Fault can be located at any point within

ZL(4). The distance to fault with respect to the substation is represented by the

distance factor dff. The proximity to the fault with respect to PV(3) is, ε(u) = dff –

df(u) |u = D = 3, then the impedance of the faulted line segment with respect to PV(3) is

ZL(4) ε(u)|u = D = 3. Conversely, the proximity to the fault with respect to PV(4) is, ε(u)

= dff – df(u) |u = ( D+1) = 4. However, df(4) > dff which means fault is upstream with

respect to PV(4) then the impedance of the fault line segment with respect to PV(4)

is ZL(4) (1- ε(u))|u = D = 3 . These impedances are added in series to ZThdn’ and ZThup

’

respectively. The following relationship can be deduced for the downstream sources

by applying KVL to the downstream sources in Figure 4.4;

( ) 3(4) ( ) (4) ( ) |ss PVdnss F ss L F L u DV I Z Z u I Z u = == + + (4.20)

( )'

3(4) ( ) (4) ( ) |ss PVdnPVdn F L F Thdn L u DV I Z u I Z Z u = == + + (4.21)

where Vss is the substation source Thevenin voltage, and VPVdn is the Thevenin

voltage of all the downstream sources. The Thevenin equivalent impedance for the

downstream side of the feeder under fault seen from the point of fault is;

'

3'(4) ( ) |ss Thdn

Thdn L u D

ss Thdn

Z ZZ Z u

Z Z = == +

+ (4.22)

Let VThdn be the Thevenin equivalent voltage for all the downstream sources

including the substation source, then the fault current contribution from all the

downstream sources including the substation source is;


1Thdn

F

Thdn

VI

Z= (4.23)

Let VPVup be the Thevenin voltage of all the upstream PV sources, then the upstream

sources can be similarly expressed as given in (4.24)

( )( )'

3(4) 1 ( ) |PVupPVup F Thup L u DV I Z Z u = == + − (4.24)

The Thevenin equivalent impedance for the upstream side of the feeder under fault

seen from the point of fault is;

( )'

3(4) 1 ( ) |Thup Thup L u DZ Z Z u = == + − (4.25)

Let VThup = VPVup be the Thevenin equivalent voltage for all the upstream PV sources,

the fault current contribution from all the upstream PV sources is;

2

Thup

F

Thup

VI

Z= (4.26)

Assuming a three-phase fault such that ZF ≠ 0, and applying KVL, the following

relationships can be derived;

( )

'

1

'

2

(4) (3)

(4) 1 (3)

Thdn Thdn L F F F

Thup F Thup L F F

V Z Z Z Z I

V Z Z Z Z I

+ + =

+ − + (4.27)

Considering that, 21 FFF III += , the Thevenin equivalent impedance at the point of

fault is;

( ) ( )( )( ) ( )( )

' '

' '

(4) (3) (4) 1 (3)

(4) (3) (4) 1 (3)

Thdn L F Thup L F

ThF

Thdn L F Thup L F

Z Z Z Z Z ZZ

Z Z Z Z Z Z

+ + + − +=

+ + + + − + (4.28)

In the case of bolted fault, ZF is removed from (4.28). Moreover, the sequence

components are appropriately connected subject to the type of fault.

Supposing VThF is the Thevenin equivalent voltage seen at the point of fault,

then the fault current at the point of fault is;

ThFF

ThF

VI

Z= (4.29)


Considering equation (4.28), the Thevenin impedance seen at the point of fault F is a

function of fault distance and level of PV penetration. Increased PV penetration will

result in reduced Thevenin impedance resulting in more fault current contribution by

the PV sources consequently lowering the fault current magnitude seen at the feeder

substation.

Figure 4.4: Per phase circuit diagram categorising PV sources into downstream and

upstream sources with respect to point of fault.

4.5 DOCAS ALGORITHM IN ADAPTIVE OVERCURRENT

PROTECTION OF RADIAL DISTRIBUTION FEEDER WITH PV

PENETRATION

In sections 4.3, 4.4 and 4.5, theoretical network analysis was done to

demonstrate the effect of PV penetration on fault current magnitude seen by the main

OC protection relay at the feeder substation. In this section, the DOCAS algorithm is

used to analyze short circuit faults, then to propose a method for making the feeder

OC protection relay threshold setting adaptive to increasing PV penetration.

Figure.4.5 illustrates the structure of the proposed scheme incorporating the

DOCAS algorithm. Mathematical derivations and the functions of the major blocks

excluding the DOCAS algorithm are presented herein. Voltage and current signals

measured at the feeder substation constitute inputs to the algorithm. Discussions

considers current signal, however is equally applicable to voltage signal as well.


Figure 4.5: The OC fault detection and diagnostic scheme incorporating the DOCAS

algorithm.

4.5.1 Exponential DC Offset

Power system signals (voltage and current) are normal distorted during a fault

condition. These signals usually contain 1) Decaying DC component, 2)

Fundamental Frequency Component, and 3) Integer harmonics as well non-integer

harmonic components. The presence of the decaying dc-offset and the harmonics

causes distortion on the fundamental frequency component, and this can affect the

accuracy of the algorithm. While the harmonic components can be effectively

filtered by appropriate low pass filters, the decaying dc-offset on the other hand is

not so straight forward. The presence of the exponentially decaying dc-offset is of

concern especially in estimating the magnitude of the fault current and/or voltage

signal.

DC Offset Removal in the Proposed Overcurrent Protection Method: The

removal of the exponentially decaying DC-offset is accomplished through the natural

process of creating the initial fault detection signal, Δf, such that Δf = ∆i is the

difference fault current signal. The method does not require any parameter estimation

and is the integral part of the fault detection process. The technique implemented in

this algorithm is based on adaption of the technique proposed by A.Rahmati et al

[50]. However, in this case two signals, the original fault signal I and the average out

of the MMF filter, Immf are subtracted to eliminate the DC-offset as opposed to the

subtraction of the even and odd samples implemented by [50]. The mathematical

derivation given is intended to describe removal of the DC-offset through the

subtraction of the signals. It does not imply the implementation of any DFT based

filter for this purpose.

Based on the equation for the discretised signal in (3.11), a discretised input

current signal is expressed in (4.30);


( )0

1

( ) sin 2H

nh h h

h

i n T I r I f n T =

= + + (4.30)

where r = e-ΔT/τ such that I0rn is the DC component with a magnitude and time

constant represented by I0 and τ respectively. The second part of (4.30) is the AC

component with Ih, fh and ϕh being the amplitude, frequency and phase angle of the

hth order harmonic with H representing the highest order harmonic in the fault

current. The phasors for the AC and DC parts of the fundamental frequency (h = 1)

component can be calculated by applying DFT as follows;

AC Part:

( )1

( ) sin 2

H

AC h h h

h

i n T I f n T =

= + (4.31)

Applying DFT to the AC part results in (4.32) [50]

1 1 1 1

1 2 1 2( ) 1 cos 1 sinacI n I n jI n

N N N N

= + + + − +

(4.32)

such that the real and imaginary parts of (4.32) are;

1 1

1 2Re ( ) 1 cosacI n I n

N N

= + +

(4.33)

1 1

1 2Im ( ) 1 sinacI n I n

N N

= − +

(4.34)

Thus, the amplitude of the AC part of the fundamental frequency component is

( ) ( )2 2

( ) Re ( ) Im ( )ac ac acI n I n I n= + (4.35)

DC Part:

0 0( )

n T

nDCi n T I r I e

−

= = (4.36)

Applying DFT to the DC part and rearranging results in (4.37) [50]


( ) nDCI n Mr= (4.37)

where,

02

2 2cos 1 sin

22

2 cos 1 1

N

r j rN N

M I r

N r rN

−

− +

=

− − −

(4.38)

such that the real and imaginary parts of (4.38) are;

0

2

22 cos 1

Re ( )2

2 cos 1 1

N n

dc

I r r rN

I n

N r rN

− −

=

− −

(4.39)

0

2

22 sin

Im ( )2

2 cos 1 1

N n

dc

I r r rN

I n

N r rN

−

=

− −

(4.40)

The fundamental frequency component of the fault signal in (4.30) can be expressed

in terms of the real and imaginary parts of the AC and DC parts are follows;

( ) ( ) ( )

Re ( ) Re ( ) Im ( ) Im ( )

ac dc

ac dc ac dc

i n T I n I n

I n I n I n I n

= +

= + + + (4.41)

Then the amplitude of the fundamental frequency component of the fault current

with exponentially decaying DC offset is;

( ) ( )2 2

_ Re ( ) Re ( ) Im ( ) Im ( )ac Fault ac dc ac dcI I n I n I n I n= + + + (4.41)

The fault current signal transformation at the decomposed MMF stages and its

reconstruction resulted in the average MMF output signal given (3.22). This signal

can be expressed as given in (4.42)

( )0_ _ _ _

1

( ) I sin 2

Hn

mmf mmf h mmf h mmf h mmf

h

i n T I r f n T =

= + + (4.42)


where I0mmfrn is the DC component of the average MMF output and the second part

of (4.42) is the AC part with Ih_mmf, fh_mmf, ϕh_mmf are the amplitude, hth order harmonic

frequency and phases angle. It must be noted that, at the MMF stages, the fault signal

results in only magnitude transformation while it is assumed that all other signal

attribute, for the DC and the AC part remain unchanged.

Applying DFT to the AC and DC parts of the average MMF output will give

the amplitude of the fundamental frequency component of the average MMF output

with exponentially decaying DC offset as;

( ) ( )2 2

_ _ _ _Re ( ) Re ( ) Im ( ) Im ( )mmf ac mmf dc mmf ac mmf dc mmfI I n I n I n I n= + + + (4.43)

The exponentially decaying DC-offset is mitigated by taking the difference between

Iac_Fault and Immf. The resulting difference current signal is the initial fault detection

signal, Δi having magnitude ΔI given by;

_ac Fault mmfI I I = − (4.44)

It must be noted that, the MMF stages emphasizes the samples at the edges of the

data window on the average MMF output. Thus, the AC signal component is also

reduced except at the edges of the data windows. The peak value of the fundamental

frequency component of Δi without the exponentially decaying DC-offset are relative

to the height of the tall edge spikes (ref Figure 3.10(c)) appearing at the regular

intervals given by (4.44).

(2 )1,....,

(2 ( 1) )

i kN Tk

i k N T

=

+ (4.44)

where k is counter and refers to the point of tall edge spikes.

4.5.2 Smoothing Filter

While the subtraction of the original fault current signal and the average output

of the MMF resulted in the elimination of the decaying component, it however

resulted in generation of high frequency ripples mainly odd harmonic components

which are shown through simulation in Figure.4.3 (c). It is important that the high

frequency ripples do not affect accurate estimation of the fault magnitude. Thus, an


appropriate low pass filter must be designed to reduce the high frequency ripples. In

this filter design, selection of any cut-off frequency is not necessary. However, a

digital filter that is time invariant and does spectral shaping, and selective frequency

filtering without assigning any cut-off frequency is desirable. An averaging filter is

one such filter, and the one used in this design is the exponentially weighted moving

average (EWMA) filter [162],[163] . The signal needs smoothing is Δi.

Given that Δi is a sequence of sampled data, lets denote the sequence as Δis.

Supposing at any instant, t = nΔT, the average of the previous samples, denoted p

can be computed as follows, [163];

( )

1

1t

tp s

s t pp

i i= − +

= (4.45)

Let’s consider the point t+1 = (n+1)ΔT where the average of the previous samples of

the data sequence can be computed as [163].

1

1

1( 1)

1

1 1

1 1

t

t s

s k p

tt

p s

s t p

i ip p

i i +

= − +

++

= − +

= +

+ +

= (4.46)

Let’s denote ( )

1

tt

s p

s t p

i p i

= − +

= , then (4.46) becomes;

( ) ( )1 1

( 1) 1 1

1 1 1

t tt p t p

tp

pi p i i i

p p pi + +

+ + = + + + +

= (4.47)

The expression for the exponentially weighted moving average filter can be obtained

by shifting the time index back one step. Thus, the expression for Δip(t) is;

( 1)( ) 1

1 1

tt p

tp

pi i

p pi −

+ + +

= (4.48)

Let’s denote,1

p

P=

+, then

1(1 )

1P= −

+, such that the exponentially weighted

moving average (EWMA) filter equation is [163].

( 1)( ) (1 )tp t

tp i ii − + − = (4.49)


The parameter, α is a weighting constant that dictates the degree of filtering, where it

can have any value within the range 0 ≤ α < 1. Moreover, in (4.49), the EWMA Δip(t)

denotes a weighted average of all past data in the data sequence Δis at point t. When

α → 0, a large number of past data points are considered in computing the EWMA

Δip(t), and as α → 1, there is less and less averaging being performed, and Δip

(t) →

Δip(t-1), and at α = 1 essentially Δip

(t) = Δip(t-1). By recognising (4.49), the EWMA

equation resembles a discrete first-order low-pass filter.

4.5.3 Phasor Estimation

The phasor estimation function of the proposed algorithm is accomplished

using the linear regressions and recursive least square estimates (RLS) techniques.

The least Square Estimate (LSE) technique is a curve fitting method used to estimate

the parameter of the system that generated the output based on a parameter

estimation model. One such model is the linear auto regression with exogenous

inputs (ARX) model given in (4.50) [164];

( ) ( ) tty T=

| (4.50)

where φ(t) is the regression vector whose elements are known and σ is the

unknown parameter vector whose elements are unknown and can be estimated by

means of LSE. Suppose y(t) is the measured data, the task is to estimate the unknown

parameters such that, y(t)-φT(t)σ is minimized. In this algorithm, the non-recursive

LSE is transformed to a recursive LSE based on the technique proposed by [165].

Referring to Figure,4.1, the input signal to LSE parameter estimation block is the

difference fault signal, ∆i, and suppose the output of the model is ŷ(t), and

considering sinusoidal nature of the power system signal, then the model output can

be described by a sum of N sinusoids as defined by (4.51)[166].

( ) ( ) =

+=N

n

ncns TnTnty1

0,0, cossin)( (4.51)

where αs,n = Ansin(δn) and αc,n = Ancos(δn) with An and δn respectively being the

amplitude and the phase angle of the nω0 frequency component. ∆T is the sampling interval

as previously defined. From (4.51), the regression vector can be defined as having elements

given by (4.52).

( ) ( ) 1,....,0sincos)( 00 −== NnTnTnt (4.52)


where φ(t) is the regression vector, and ωo is the fundamental frequency. The

elements of the regression vector are known since ω0 and ∆T are known. The

unknown parameters vector whose elements are the coefficients of the sine and

cosine terms of (4.51), to be estimated by (4.53)

1,...,0,, −== Nnncns (4.53)

where σ is the unknown parameter vector. From a measured data window of N

samples of the differential fault signal, ∆f, the elements of the unknown parameter

vector can be estimated by the non-recursive least square method as given in (4.54)

( )1

T T i −

= (4.54)

where (φTφ)-1φT is the pseudoinverse of the regression vector. Let σN-1 denote

the non-recursive estimate of the unknown parameter, then (4.54) can be written as

(4.55). The unknown parameter can now be estimated recursively using (4.56) [165]

( )1

1N T T i −

− = (4.55)

( ) ( ) ( ) 1 1N N NG n i n n − −= + − (4.56)

where (∆i(n) - φ(n)σN-1) is the error between the model output and the

measured data at the sample point n∆T. G(n) is an element of the time varying gain

vector G at the same sample point. The elements of G are recursively updated

according to (4.57) [165]

( ) ( )( ) ( )( ) ( ) ( )( )( ) ( )( ) 111 −−−+=

TTTT nnnInnnG (4.57)

Using the estimated parameters, the instantaneous fault signal at any sample

point, n∆T can be calculated using (4.58); and the instantaneous magnitude and the

phase angle is determined using (4.59) and (4.60) respectively.

( ) ( ) ( ) ( ) ( )nnnnnf ccss

+= (4.58)

( ) ( )( ) ( )( )22nnnF sc += (4.59)


( )

= −

c

sn

1tan (4.60)

where αs(n) = ∆I(n)sin(δ(n)) and αc(n) = ∆I(n)cos(δ(n)) are the instantaneous

magnitudes of the sine and cosine terms respectively of the differential fault current

signal at instant n∆T.

4.6 APPLICATION OF MFD OUTPUT IN ADAPTIVE RADIAL

DISTRIBUTION FEEDER OC PROTECTION

The DOCAS algorithm continuously monitors the network condition by testing

the current signal against a threshold metric. The threshold metric has minimum and

maximum denoted IThresholdmin and IThresholdmax respectively where IThreshold is the current

magnitude threshold to detect and declare a fault condition. If the fault current

magnitude, denoted IFault is such that, IFault > IThresholdmin, a disturbance detection (DD)

flag is activated to alert the algorithm of an abnormal condition. IFault is further tested

against the IThreshold and if IFault > IThresholdmax then level 1, (L1) flag is activated to

indicate overcurrent (OC) fault and OC protection system is activated to act on this

fault. If IFault < IThresholdmax the level 2, (L2) flag is activated to initiate the HIF

detection and classification procedure.

4.6.1 Adaptive Overcurrent Threshold Value

The OC Threshold parameter is used to discriminate the disturbance between a OC

fault and other operation conditions. The current signal is the primary input quantity

to detect any disturbance. The parameter in (3.39) are redefined as, Imax(w) = IFault,

and Imax(w + 1) = IPrefault, such that the per unit (pu) increase fault current magnitude

is defined by (4.61).

( )Fault Prefault

inc

Prefault

I II

I

−= (4.61)

It is assumed that, under OC fault conditions, the current magnitude increases.

In the proposed adaptive OC protection scheme, the pu increase, Iinc is used to detect

and declare the fault. From (4.61), Iinc can have a minimum and maximum value of 0

and ≥ 1.0 per unit respectively where Iinc = 0 means no disturbance, and Iinc ≥ 1

means disturbance has occurred but may not be OC fault. Considering that


overcurrent threshold is set about 2 to 3 times the prefault load current, the OC

threshold value based on current magnitude is IThreshold = 2IPrefault, such that;

2

FaultThreshold

Prefault

IM

I= (4.62)

where MThreshold is the ratio of the fault current IFault to IThreshold and is taken as OC

threshold parameter in the proposed scheme. Using (4.62), MThreshold can have a

minimum and maximum of 0.5 and ≥ 1.0 per unit and correspond to MThresholdmin and

MThresholdmax respectively. The increase in current magnitude, Iinc is compared with

MThreshold to declare a fault. The following scaling factor is applied to Iinc for

comparison, MThresholdmin(1+Iinc) such that, Iincmin = 0.5(1+0) = 0.5 and Iincmax =

0.5(1+1) = 1.0. Considering noise and other factors including transient disturbances,

the threshold is set at 15% above prefault value such that MThresholdmin = 0.575 and

MThresholdmax = 1.15 per unit. It must be noted that the 15% above minimum and

maximum values of MThreshold for noise consideration is not based on any criteria,

however has been selected based on the value used by Gautam and Brahma [78] .

This value is subject to review for different fault conditions, network configuration,

level of DER penetration, noise level, etc. This, for initial fault detection, if 0.575 <

MThreshold < 1.15, then DD flag is activated, followed by L2 flag to initiate HIF

feature extraction and classification process discussed in Chapter 5. For OC

parameter setting, the maximum value is considered, thus a fault current increase of

greater than or equal to 1.15 pu must be detected to initiate the OC protection.

4.6.2 Overcurrent Fault Detection Timer and Counter Parameters

Supposing a fault occurs such that, Iinc > 1.15, then Timer and Counter

parameters are used to count the consecutive number of tall edge MFD spikes with

increased heights above the OC MThresholdmax of 1.15 pu within OC fault detection

delay (Tdmax) of 15.3125ms. This is the time delay in which a fault must be declared

and is considered as the waiting time. Moreover, within the fault detection time

delay, a minimum of two MFDTall spikes must be detected to declare OC fault. To set

the counter value, the following has been considered. Within Tdmax, there are five

edge spikes, and the edge spikes can come in the following combinations, two

MFDTall spikes with three MFDShort spikes or three MFDTall spikes with two MFDShort

spikes. Considering that fault detection is based on MFDTall spikes, a minimum of


two MFDTall spikes exist, hence, the counter value is set at two. It must be noted that,

if two tall edge spikes are detected before Tdmax lapses, the algorithm will not activate

L1 flag, both Counter and Timer conditions must be satisfied for the OC protection

procedure to proceed any further.

Figure 4.6: Flowchart showing the OC fault detection process

4.7 SIMULATIONS AND DISCUSSION

The impact of PV penetration on the fault current magnitude at the feeder

substation, and its implications on the feeder OC protection are demonstrated with

two cases. The simulated conditions are analysed with the proposed algorithm to

demonstrate its attributes in fault detection.


4.7.1 Simulation System Description

To test and verify the effectiveness of the proposed adaptive overcurrent

protection scheme for radial distribution network feeder, and test system was

modelled in MATLAB/Simulink. The layout of the testbed for simulations to analyse

the impact of PV penetration on radial distribution feeder overcurrent protection is

shown in Figure 4.7. The testbed was modelled using power system device objects

available in Simscape tool box in the Simulink library. The distribution feeder was

modelled to operate at a nominal system voltage of 25kV with system frequency of

50Hz. The synchronous generator source at the substation represents power supply

from the grid, and has total generating capacity of 5MW. The total distance of the

feeder is 30km, thus a short line model is used for each line segment between the PV

systems with positive- and zero- sequence impedances, z(1) = 0.1153 + j0.53 Ω/km

and z(0) = 0.413 + j1.043 Ω/km respectively. The system is comprised of four PV

systems connected along the feeder length. The distance to PV systems from the

feeder substation are; PV1 = 0.5 km, PV2 = 10 km, PV2 = 20 km and PV4 = 30 km.

Each tapped load is P = 1.1MW and Q = 0.15 Mvar. The complete distribution feeder

system with PV penetration incorporates measurement, interfacing as well as control

devices.

Figure 4.7: Test feeder for modelled in Simulink for simulations

4.7.2 Characteristics of the Test System Components:

Instrument transformers: Instrument transformers include voltage transformer

(VT) as well as current transformer (CT). These are located at the substation, and the

fault signal inputs are taken from the secondary of the VT and CT. While both types

of instrument transformers are shown, the OC protection uses CT to measure the

PV1 PV2 PV3

Substation

25kV,

50Hz

PCC

V, I Measurements

PV4

Load

260V/25kV

50Hz

Δ/Y

DOCAS

CT

VT

SS

LoadLoadLoad Load

FP2FP1 FP3 FP4


fault current signal as input to the DOCAS algorithm. In the simulation, the CT is

model with turns ratio of 500:1 and operates in the linear of the saturation

characteristic curve. The efficiency of the CT has not been considered in the

modelling on the assumption that, losses are minimal, thus CT operates at near rated

efficiency.

Distribution transformers: Each PV system is connected to the main feeder

through a 600kVA, 50 Hz, 260V/25kV delta/wye distribution transformer with wye

grounded. The transformer generally provides a galvanic isolation between the PV

system and main feeder.

Photovoltaic Systems: Each PV system is modelled identically to generate

500kW at 260V DC. A PV system is comprised of five 100kW PV strings. Each 100

kW PV string has 64 parallel strings with 5 series connected PV modules. The PV

module used are SunPower SPR-315E-WHT-D found in the Simscape library in

MATLAB/Simulink [167] with following characteristics; maximum power =

315.072 W, open circuit voltage, Voc = 64.4 V, short circuit current, Isc = 6.14 A.

The voltage and current at maximum power point are Vmpp = 54.7 V and Impp = 5.76

A. The equation that models the current output of each PV module is give in (4.1)

[168], [169].

exp 1s spp PV o

t ss p

V IR V IRI N I I

V N R

+ + = − − −

(4.63)

where Nss and Npp are number of series and parallel connected cells in the PV

module. The parameters are defined as follows:

IPV is the current generated by the incident light

Io is the diode reverse saturation current

Vt is the thermal voltage of PV module, such that Vt = NsskT/q, where k

is the Boltzmann constant (1.3806503 x 10-23 J/K), q is the electron

charge (1.60217646 x 10-19 C), T is the temperature of the p-j

junction.

Rs, RP are the series resistance and parallel resistances respectively.

The current versus voltage (I-V) and power versus voltage (P-V) curves at

standard test condition (STC), 1000W/m2, 25ºC for each 100kW PV string is shown


in Figure.4.8. The system voltage (VMPP) and current (IMPP) at maximum power point

(MPP) respectively are 273.5 V DC and 368.6 A.

Figure 4.8: Characteristic curves for the PV strings at STC, (a) I-V and (b) P-V curves

DC-DC boost converters: The PV system DC voltage is unregulated, and

fluctuates with variation in solar irradiation and temperature [170]. Thus, to maintain

a stable voltage at constant value, a DC-DC converter is required. The inverter

requires input voltage of 500 V DC; thus a DC-DC boost converter was used to boost

the PV string voltage from 270 V to 500 V DC. The DC-DC boost converter used in

the test system is an averaging switch-mode converter using pulse-width modulation

(PWM) with voltage control. It is not within the scope of this research to provide

comprehensive analysis of available DC-DC converter technology. However, the

intention is to state the function of the converter and type used in the model. Thus, a

brief theoretical background on the averaged DC-DC converter is provided. A circuit

describing the topology of a boost converter with MOSFET and diode switching is

shown Figure 4.9.

Figure 4.9: Circuit topology of a DC-DC boost converter [171]


To analyse the boost converter circuit in Figure 4.9, we consider the switching

intervals of Q1 and D1.

Interval 1: Q1 is in “on” state and D1 “off”, the sections of the circuit after and

before the diode become isolated. The inductor L is shorted to ground. Under this

condition we derive the following equations [171];

L inv V= (4.64)

outC

Vi

R= − (4.65)

Interval 2: Q1 is in “off” state and D1 “on”, the inductor L is in series with

diode. Under this condition we derive the following equations;

L in outv V V= − (4.66)

outC L

Vi i

R= − (4.67)

It is assumed that voltage and current ripples are very small, thus iL = I; then

outC

Vi I

R= − (4.68)

Using the switching intervals, and the voltage across the inductor, vL(t) and the

capacitor through current, iC(t) can be represented graphically as shown in Figures

4.10(a) and (b) respectively. The area under the curves in the plots of Figure 4.10(a)

and (b) are equated to zero in steady state as given by (4.69) and (4.70) respective.

Figure 4.10: Switching waveforms for the voltage and current in the DC-DC boost converter


0 ( ) (1 )( )in in outD V D V V= + − − (4.69)

0 ( ) (1 )( )out outV VD D I

R R= − + − − (4.70)

The parameter D is the on/off duty cycle where D = Vout/Vin = Ton/Toff. The steady

state output voltage and the inductor current can be respectively solved from (4.69)

and (4.70) as defined by equations (4.71) and (4.72) respectively.

1

1out inV V

D=

− (4.71)

( )2 2

1 1 1

1 11

out in inV V VI

D R R RDD= = =

− +− (4.72)

Equation (4.71) shows that, by increasing D in the range, 0 < D < 1, the output

voltage can be made larger than the input voltage. At D = 0, the output voltage is

equal to the input voltage. The output current is determined according to (4.71), and

by inspection, the output current will be lower than the input current for the same

value of D applied in both equations.

In PV systems, the voltage and current are unregulated due to the changing

solar irradiance and temperature. This can be visualised by varying the temperature

in the 100kW PV string in the test system. Plots of I-V and P-V curves at

temperatures of 25ºC, 30ºC and 40ºC for the 100kW strings are shown in Figure

4.11.

Figure 4.11: Characteristic curves for the PV strings at STC, (a) I-V and (b) P-V curves

with increased temperatures


Under test these conditions the PV strings operate at lower MPP where the

respective VMPP are; 273.5 V, 268.6 V and 259.5V. The decrease in power is related

to the temperature coefficient of material in the PV modules [172]. There is minimal

change in the current due to temperature change where the respective currents at the

new MPP due to changing temperature are; 368.6 A, 369.7 A, and 370.9 A. The

impact of current is normally significant with changing irradiance. Under different

ambient conditions (change in temperature and irradiance) which the PV systems are

subjected to, the MPP changes. It is expected that under these conditions, the DC bus

voltage must be kept constant. This can be achieved by appropriately scaling the

value of D in equation (4.8).

Maximum Power Point Tracking (MPPT): In PV systems, the MPPT

algorithm is used in conjunction with the DC-DC boost converter by appropriately

adjusting the value of D to maintain a constant DC voltage output at different MMP.

Several different MPPT algorithms have been proposed, including Perturb and

Observe, Incremental Conductance and Fuzzy logic methods [173], [174]. The

MPPT algorithm used in the simulation system is the Perturb and Observe (P&O)

algorithm. The choice of this algorithm was based on simplicity and speed of

execution based on only two inputs as compared to the other popular method, the

incremental conductance method which requires four input parameters including

incremental and instantaneous PV array conductance and VMPP and IMPP [175], [176].

Moreover, the P&O algorithm was selected on the assumption that the simulated

faults occur under steady state environmental conditions (slowly changing irradiance

and temperature). Furthermore, the algorithm is still widely used. The inherent lack

of speed in tracking the MPP and loss of power when subjected to rapidly changing

irradiance and temperature widely reported in literature regarding the P&O algorithm

can be reduced by increasing the sampling rate and the execution speed [177],[178].

The P&O algorithm works by periodical perturbation (increment or decrement)

of the array voltage and comparing the array output power with power output of the

previous perturbation cycle. If the power increases, the perturbation will continue in

the incremental direction in the next perturbation cycle, if otherwise, the perturbation

will reverse and go in the decremented direction. This process continues until the

new maximum power point is reached. To further illustrate the P&O algorithm,

supposing RMPP is the array characteristic impedance, such that RMPP = VMPP/IMPP,

and R is the array load impedance, for maximum power transfer to occur, RMPP = R.


RMPP depends on the operating environmental conditions, irradiance and temperature.

The P&O algorithm either increment or decrement the duty cycle D to control VMPP

to match RMPP to R.

DC-AC Inverter: The DC to AC converter used to interface the PV systems to

the radial feeder via the distribution transformer is the Voltage Source Converter

(VSC)[179] . The model used is the two-level average VSC model with six pulse

insulated gate bipolar transistor with inverse-parallel diodes. The VSC takes the 500

V DC from the DC-DC boost converter and converts it to 260 V AC, three-phase, 50

Hz output. A brief description of the VSC operation is provided herein. Figure 4.12

shows topology of the two-level VSC and the AC voltage output at one of the

phases. The two-level VSC model is an average model. As it is obvious from the

graph of output waveform, the positive and negative halves of the AC voltage output

are relative to the positive and negative half values of the DC input voltage taken at

the midpoint. There are three legs in the SVC topology with upper and lower IGBT

switches, S1 to S6. These legs, call phase-legs generate the phase AC voltages by

appropriate (complimentary) switching of the upper and lower IGBTs connected to

that leg. S1, S3 and S5 are the upper switches for phases A, B and C respectively

while S2, S4 and S6 are the lower switches for the respective phases. All the upper

switches are simultaneously in the “On” state for 180º (half fundamental period) with

phase shift of 60º between each other obtained through successive gating signal

between each IGBT. In the next 180º all the upper IGBT switches are in the “Off”

state and the lower IGBT switches are in the “On” state. Any simultaneous switch on

of the upper and lower IGBT switches in a phase-leg will result in short circuiting

that leg, thus this is avoided. The magnitude of the fundamental component of

generated AC output voltages at each phase is controlled by pulse width modulation

(PWM). Moreover, the PWM reduces harmonic content present in the voltage (and

current) signal.


Figure 4.12:Circuit topology of two-level voltage source converter with a phase output

voltage waveform [179]

4.7.3 DC-offset Removal and Smoothing Filter

Referring to the block diagram layout of the OC fault detection system, the

fault current input signal undergoes several condition stages including DC offset

removal, smoothing ripples and fault current magnitude estimation. These processes

are demonstrated through simulation by considering a SLG on phase A of the test

system.

DC-offset Removal: In Figure 4.13(a), the fault current and the average MMF

output for a SLG fault are shown. The difference fault current signal, ∆i is shown in

Figure 4.13(b). The respective power spectral density plot for each signal is shown in

Figure 4.14. The fault current signal and the average MMF output show presence of

the exponentially decaying DC offset. This is confirmed by their respective power

spectral density (PSD) plots. The difference fault current signal shows no sign of

exponentially decaying DC offset. This can be confirmed by its PSD plot in Figure

4.14(c). However, the subtraction process in creating ∆i resulted in high frequency

ripples of odd harmonics as shown in the PSD plot.


Figure 4.13: Fault current signals with DC-offset, (a) The fault current signal and average

MMF output (b) Difference fault current signal.

Figure 4.14: Power spectral density plots for (a) fault current (b) average MMF output and

(c) difference fault current, ∆i.

Exponentially Weighted Moving Average Filter Output: An exponentially

weighted moving average (EWMA) filter was designed to smooth out the ripples in

the difference fault current signal. A weighted value of α = 0.2 was experimental

determine to give the best compromise between the harmonic distortion and delay.

The output of the EWMA filter is shown in Figure 4.15. Generally, a delay can be

noticed in the filtered output, however at the edges the two signals are almost in

phase. The edges determine the increase fault current magnitude.

The PSD plot of the EWMA filter output signal is shown in Figure 4.16. The

PSD plot shows significant reduction in the power of the odd harmonics, moreover,

the DC-offset has been eliminated.


Figure 4.15: Difference fault current signal, ∆i and the EWMA filter output

Figure 4.16: Power spectral density of the EWMA filter output

Recursive Least Square Filter Output: The EWMA filtered version of the

difference fault current signal, ∆i provides input to the recursive least error square

filter for phasor estimation. The input and the output signals are presented in Figure

4.17 for comparison. It shows that the two signals are in phase and almost exact

replication of each other. Thus, it can be concluded that the magnitude estimation is

accurate.

Figure 4.17: RLSE filter signals (a)RLSE filter input signal, (b) RLSE filter output signal

4.7.4 Effect of Increased PV Penetration on Fault Current Magnitude

The effect of increasing PV penetration level on the feeder current magnitude

was simulated by decreasing the current contribution from the grid side by

decrementing the substation source power contribution while maintaining a fixed


lumped load at 5 MW. The substation source has a total capacity of 5 MW while the

total PV generation is 2 MW, thus decrementing the substation source contribution

allows for increased PV contribution to maintain the 5 MW supply. The PV systems

were maintained at a fixed irradiance of 300 W/m2 at 30°C for all simulations.

Prefault Condition: The substation source was decremented to allow

increasing PV penetration at 0, 28, 33, 44 and 50 percent. The MFD values for

prefault current magnitudes at these PV penetration levels for each phase of the

feeder are given in Table 4.1, and the results show that as the PV penetration level

increases, the MFD values decrease indicating decrease in the current magnitude at

the substation.

Table 4.1: Prefault current MFD values

Fault

Types Phase

MFD Output – Fault Current Magnitudes at

PV Penetration Level (%)

0 28 33 44 50

Prefault

A 0.0032 0.0013 0.0011 0.0010 0.0009

B 0.0042 0.0015 0.0013 0.0011 0.0011

C 0.0041 0.0014 0.0014 0.0013 0.0013

Feeder Fault Current Level without PV Penetration: several short circuit

fault conditions including, single line-to-ground, (SLG), line-to-line-ground (LLG)

and three-phase (3-ph) faults were simulated at locations 1, 2, 3 and 4 respectively

designated FP1, FP2, FP3 and FP4 on the simulation system. The MFD values of the

fault current magnitude for each type of simulated fault are tabulated in Table 4.2.

The results in Table 4.2 are consistent and reflect the fact that current

magnitude reduces due to the increasing line impedance as the fault distance

increase. However, it must be noted that, since there is no PV penetration, and the

only influencing factor is the Thevenin equivalent line impedance seen at the fault

location assuming the fault impedance is zero.

Table 4.2: Fault current MFD values at fault locations along feeder length.

PV

Level

(%)

Fault

Types Phase

MFD Output-Fault Current Mag

Fault Locations

1 2 3 4

0

SLG A 0.0102 0.0096 0.0091 0.0086

LLG A 0.0091 0.0088 0.0084 0.0082

B 0.0101 0.0098 0.0091 0.0086

3-Ph

A 0.0084 0.0082 0.0080 0.0078

B 0.0080 0.0079 0.0077 0.0075

C 0.0078 0.0077 0.0074 0.0072


Using the MFD data recorded in Tables 4.1 and 4.2, the fault current increase in each

phase can be calculated using (4.61) for different faults types at the specified location

for case. Cases of fault current increase in phase A under the simulated fault

conditions without PV penetration are given in Table 4.3. The other two phases can

be treated in the same manner. The results show that, even without any PV

penetration, the fault current magnitude decreases with increased fault distance.

Table 4.3: Increase in fault current magnitude at different fault location

Fault

Types

Increase in the fault current magnitude

for 0% PV at different locations

1 2 3 4

SLG 2.1875 2.0000 1.8438 1.6875

LLG 1.8438 1.7500 1.6250 1.5625

3-Ph 1.6250 1.5625 1.5000 1.4375

Feeder Fault Current Level with PV Penetration: Different fault conditions,

including SLG, LLG and 3-ph faults were simulated at fault location 2 with different

PV penetration level. The results of the MFD outputs are given in Table 4.4. where

the results show the trend already established where increasing PV penetration

results in reduction in fault current magnitude at the feeder substation. To further

illustrate this trend, magnitude output of the RLSE filter and the MFD are shown in

Figures 4.18 and 4.19 respectively for PV penetration levels of 0%, 28% and 33%.

Table 4.4: MFD values fault currents for faults at Fault location 2

Fault

Types Phase

MFD Output – Fault Current Magnitudes at

PV Penetration Level (%)

0 28 33 44 50

SLG A 0.0096 0.0086 0.0071 0.0060 0.0053

LLG A 0.0088 0.0083 0.0077 0.0059 0.0047

B 0.0098 0.0082 0.0067 0.0055 0.0054

3-Ph

A 0.0082 0.0081 0.0065 0.0050 0.0042

B 0.0079 0.0077 0.0062 0.0052 0.0040

C 0.0077 0.0071 0.0064 0.0040 0.0039


Figure 4.18: RLSE filter magnitude response for SLG fault at fault location 2 at 0%, 28%

and 33% PV penetration

Figure 4.19: MFD output corresponding to fault current magnitude for SLG fault at fault

location 2 at 0%, 28% and 33% PV penetration.

Application of equation (4.61) to calculate the level of increase in fault current

magnitude for distribution feeder will give an incorrect trend with increasing PV

penetration as the fault current magnitude increases with increased PV penetration.

This trend is demonstrated by applying equation (4.61) to MFD values for phase A

current for the different faults in Table 4.4. The results of the calculation are shown

in Table 4.5.

Table 4.5: Fault current increase at various PV levels for faults at location 2

Fault

Types

Inc. in Fault Mag. using (4.61)

at given PV Levels (%)

0 28 33 44 50

SLG 2.0000 5.6154 5.4545 5.0000 4.8889

LLG 1.7500 5.3846 6.0000 4.9000 4.2222

3-Ph 1.5625 5.2308 4.9091 4.0000 3.6667

The maximum fault current under any fault condition would occur at fault

location nearest to the relay, and in this case nearest to the feeder substation, and at

zero PV penetration. The maximum fault current occurs at location 1 (FP1) at 0%

PV, thus referring back to the results in Table 4.3, the highest increase in fault

current magnitude is 2.1875 pu for SLG fault on phase A. All increase in fault


current magnitudes in phase A will be lower than this value, thus this is defined as

Iincmax. With increasing PV penetration, any fault along the feeder length will result in

lower current magnitude compared to Iincmax. To account for this, fault increases at

different PV penetration levels and fault locations are made relative to Iincmax. The

relative increase in fault current magnitude from without PV penetration to with PV

penetration can be computed according to (4.73);

( )

max

( _ )

Fault PV

incRel inc

Fault No PV

II I

I= (4.73)

where IincRel. is the increase in fault current magnitude relative to the maximum

fault current increase without PV penetration. IFault(PV) and IFault(No_PV) are the fault

current magnitudes with and without PV penetration respectively represented by

respective MFD values.

Using MFD data in Tables 4.1 and 4.4, the relative increase in fault current

magnitude at the given PV penetration levels for various fault conditions at fault

location 2, calculated using (4.73) are given in Table 4.6. The trend shown by the

results in Table 4.6 indicate that, the fault current magnitude decreases with

increased PV penetration level.

Table 4.6: Increase fault current magnitude at different PV level for fault a location 2

Fault

Types

Relative pu Increase in Fault Current Magnitude

at given PV Levels using (4.73)

0% 28% 33% 44% 50%

SLG 2.0000 1.7917 1.4792 1.2500 1.1042

LLG 1.7500 1.6506 1.5313 1.1733 0.9347

3-Ph 1.5625 1.5434 1.2386 0.9527 0.8003

Supposing the main feeder relay has total reach up to location 4, then a fixed

pu threshold (pickup) value using the prefault and fault MFD values for the same

fault at location 4 in Tables 4.1 and 4.2 respectively can be calculated using (4.62)

giving 1.4015625 pu. A trend can be observed by comparing the different pu fault

current increases in Table 4.6 with the fixed threshold value which suggests that the

relay will under reach for some fault conditions at 33% PV penetration and all faults

occurring at 44% and 50% PV penetration levels for faults at location 2. This trend is

graphically illustrated in Figure 4.20. In Figure 4.20(a), the MFD values for the fault

current magnitude decrease with increased PV penetration. Similarly, the pu fault

level increase using (4.73) in Figure 4.20(b) decreases with increased PV


penetration, and begins to under reach at 33% PV where the fault increase drop

below Mpickup which is a fixed threshold. In Figure 4.20(c), a different trend is

established using (4.61). The trend generally shows that the pu increase in fault

current magnitude decreases with increased PV penetration. However, the pu

increase in fault current magnitude is significantly higher than the actual increase in

fault magnitude as indicated by the trend in Figure 4.20(b). This trend helps define

the basis for the adaptive OC protection scheme developed in this thesis and

described in section 4.8.


location 2 at 0%, 28% and 33%, 44% and 50% PV penetration levels.

4.7.5 Effect of Increased Fault Distance with PV Penetration on Fault Current

Magnitude

The same fault conditions in subsection 4.74 were simulated at different fault

points and currents measurements were recorded 28% PV level. The distance to the

fault locations from the substation are; FP1 = 7.5 km, FP2 = 12.5 km, FP3 = 17.5 km

and FP4 = 25.5 km.

Feeder Fault Current Level with Increased Fault Distance with PV

Penetration: Fault conditions were simulated at fault location 1, 2, 3 and 4

respectively designated FP1, FP2, FP3 and FP4 along the feeder. The MFD values for

the fault current seen by the substation feeder relay are tabulated in Table 4.7. The

decrease in fault current magnitude with fault distance under PV penetration can be

observed in the results and demonstrated graphically in Figures 4.21 and 4.22.


Figure 4.21: RLSE filter magnitude response for SLG fault at fault locations 1, 2 and 3 at

28%


locations 1,2 and 3 at 28% PV penetration.

Application of (4.61) to establish the general trend on the impact of increased

fault distance under PV penetration gives an incorrect scenario as demonstrated by

the results in Table 4.8. The increase in fault current seems high; however, the actual

fault current magnitude is still lower than the fault current for the SLG fault at

location 1 without PV. The general trend shows decrease in fault current magnitude

as fault distance increases.

Table 4.7: Fault level increase at different fault location at 28% PV penetration

Fault

Types

Increase in Fault Current Magnitude relative to maximum fault current

at 28% PV at given Locations using (4.73)

1 2 3 4

SLG 1.9945 1.7917 1.6209 1.4520

LLG 1.8032 1.6506 1.4702 1.3529

3-Ph 1.6057 1.5434 1.4438 1.3638

The impact on the fault current can be further analysed by using (4.73) to

calculate the relative increase in fault current magnitude with increased fault distance

under PV penetration. The results from this calculation are tabulated in Table 4.9,

and graphically illustrated in Figure 4.23.

Figure 4.23(a) shows the decrease in fault current magnitude due to increased

fault distance without PV penetration using (4.73). It can be observed that the relay


can maintain its distance of coverage without under reaching. Figure 4.23(b) shows

similar trend as in Figure 4.23(a) the case of increased fault distance with 28% PV

penetration. However, in this case the reach of the relay is decreased and the relay

under reaches at location for the LLG and 3-Ph faults when a fixed pick up threshold

is applied. The reach of the relay is affected by increased PV penetration, and as the

distance to fault increases, the relay will not be able to maintain its coverage of the

feeder.

Consider the case of the SLG fault, at 0% PV penetration, a SLG fault at

location 1 registered an increase of 2.1875 pu (Iincmax), and at location 4, the same

type of fault registered an increase in fault current magnitude of 1.6875 as given in

Table 4.3. This is a decrease of 23%. Consider the same fault at 28% PV penetration.

At fault location 1, the increase is 1.9945 and at fault location 4 the increase is

1.4520 pu as shown in Table 4.9. This is a decrease of 25%. When considered in

terms of Iincmax, the SLG fault at location 4 with 28% PV penetration results in a

reduced fault magnitude of 38%. This trend shows that increased PV penetration will

have an impact on the existing feeder protection system device coordination based on

passive unidirectional current flow. Moreover, it also shows that the relay will under

reach much faster than it would have without PV penetration.

While it was shown that fault current magnitude decreases with increased PV

penetration, a different trend was observed when directly applying (4.61), where the

increase in fault current magnitude is quite significant even with PV penetration as

was shown in Tables 4.5 and 4.8, and graphically illustrated in Figure 4.23(c). The

increase in fault current magnitude determined using equation (4.61) is adaptive to

any change in current magnitude influenced by PV penetration, load change and

network topology change, etc. Moreover, the threshold parameter defined in equation

(4.62) is made adaptive due to the memory update every 9.6875ms defined in section

3.5.5. Thus, a scheme for adaptive inverse time overcurrent (ITOC) relaying based

on equations (4.61) and (4.62) and applying appropriate scaling to the fault current

increases determine using equation (4.73) given in Tables 4.6 and 4.9 can be

designed and appropriately matched to a standard ITOC relay curve.


Table 4.8: Increase in fault current magnitude at different fault distance 28% PV

Fault

Types

Inc. in Fault Mag. using (4.61)

for 28% PV at given Locations

1 2 3 4

SLG 6.1538 5.6154 5.2538 4.6923

LLG 5.8446 5.3846 4.8462 4.4625

3-Ph 5.3846 5.2308 4.9231 4.6923


location 2 at (a) 0% and 28% PV penetration.

4.8 INVERSE-TIME OVER CURRENT RELAYING USING MFD OUTPUT

SIGNAL

The inverse time overcurrent relaying (ITOC) parameters for adaptive feeder

overcurrent protection are determined as defined herein.

4.8.1 Relay Pickup Parameters

In the OC protection scheme, a disturbance must be detected and tested against a

threshold constraint to determine if the disturbance is a fault within a certain time

constraint before trip signal can be issued.

A OC fault condition is said to exists if Iinc > MThreshold, Therefore, this

condition must be satisfied before the procedure to determine the relay trip time

described herein can be initiated. The increase in current magnitude, Iinc based on the

MFDTall values was determine as Iinc = (Imax(w) – Imax(w + 1)) / Imax(w + 1) = (IFault -

IPrefault) / IPrefault. The relay pickup parameters also based on the MFDTall values are

defined as follows; │I│= IFault and │Ipickup│= 2IPrefault) such that; M =| I |/|


Ipickup |. The values of M must fall within a range defined by Mmin < M < Mmax, where

these are defined as follows;

Mmin = 1, where | I | = | Ipickup |,

Mmax = IFault_No_PV(max)/2IPrefault_No_PV(max)

The minimum value of Mpickup is set at 15 percent above Mmin, such that, Mpickup =

1.15Mmin. The value for Mmax is determined by means of power flow algorithm and is

updated only when major network changes occur. Moreover, the Mmax is based on the

prefault and fault current values without the penetration of PV such that,

IFault_No_PV(max) is the maximum fault current without PV and IPrefault_No_PV(max) is the

maximum prefault current without PV.

Increased penetration of the PV reduces the fault current magnitude on the

feeder, thus to account for this, all values of M are calculated as a ratio of Mmax

defined by (4.74),

_

max

_ _ (max)

Fault PV

Rel

Fault No PV

IM M

I= (4.74)

where MRel. is the value of M relative to Mmax and IFault_PV is the fault current with PV

penetration. It must be noted that, for all fault conditions at any point on the feeder

under any PV penetration level, load condition or network topology, the value of

Mpickup has to be maintained. This is the minimum point on the Inverse Time

Overcurrent (ITOC) relay curve. Therefore, all other M values must be shifted to the

right on the relay curve (above Mpickup) to compensate for the reduction in fault

current magnitude with increasing PV level. The compensated value of M is then

given by (4.75);

Comp Rel pickupM M M= + (4.75)

where MComp is compensated M value considering the dynamic variation in

feeder current magnitude influenced by factors such different PV penetration level,

load condition, network topology change.

The case for SLG fault on phase A at different PV penetration levels and fault

locations can be used to demonstrate the proposed ITOC relaying strategy for

adaptive feeder OC protection. For this purpose, the value of Mmax using the MFD


values for prefault current recorded in Table 4.1 and MFD value for the maximum

fault current without PV penetration for the SLG fault at location 1 given in Table

4.2 is, (0.0102 / (2 x 0.0032)) = 1.59375. Following the procedure defined in (4.74)

and (4,75), the fully compensated MComp values for the SLG faults, taking into

consideration the effect of increased PV penetration and fault distance are calculated

and tabulated in Table 4.10 and Table 4.11 respectively. The MComp values are

adaptive, however they must operate on a certain relay operating curve to determine

the relay operating time.

4.8.2 Relay Operating Time

To determine appropriate relay operating times, the MComp values must be

plotted on to a standard digital relay curve. The standard moderately inverse ITOC

relay curve defined by (2.1) with current tap setting (CTS) parameters, A = 0.0515, B

= 0.1140 and ρ = 0.02 given in Table 2.1 and TDS = 1 shown in Figure 4.24 is used

to demonstrate this. The relay operating range is defined as Mpickup < MComp <

MCompmax. The Mmax value without PV penetration was determined to be 1.59375, and

following the procedure defined in (4.74) and (4.75) the compensated maximum M

value is 2.74375. Therefore, MComp values must fall within the range defined by 1.15

< MComp < 2.74375. All values of MComp in Tables 4.10 and 4.11 fall within this

range, and therefore can be easily plotted on this curve. It is noted that, at different

values of Ipickup (=2IPrefaul), Mmin = I/Ipickup is always 1 giving the same Mpickup =

1.15Mmin. The maximum relay pickup time (Ttripmax) corresponds to Mpickup and is

maintain irrespective of the PV penetration level or fault location.

Figure 4.24: Standard moderately inverse ITOC relay curve with M values in Table 4.10


Table 4.9: Per unit increases (M) in fault current magnitude at different PV levels at FP2

MFD Trip

Parameters

Fault at different PV Levels at Location 2 (FP2)

0% 28% 33% 44% 50%

Ipickup 0.0064 0.0026 0.0022 0.0022 0.0018

M 1.5000 3.3077 3.2273 3.0000 2.9444

MRel. 1.5000 1.3438 1.1094 0.9375 0.8281

MComp 2.6500 2.4938 2.2594 2.0875 1.9781

Table 4.10: Per unit increases (M) in fault current magnitude at different fault locations

MFD Trip

Parameters

Fault at different Locations for 28% PV

1 2 3 4

Ipickup 0.0026 0.0026 0.0026 0.0026

M 3.5769 3.3077 3.0769 2.8462

MRel. 1.4531 1.3438 1.2500 1.1563

MComp 2.6031 2.4938 2.4000 2.3063

The corresponding operating times for the SLG fault under consideration are

shown in Tables 4.12 and 4.13 for PV penetration level and fault distance. Note that

the maximum trip time (Ttripmax) is 18.512s which corresponds to Mpickup = 1.15Mmin

irrespective of the PV penetration level and fault distance. Moreover, the minimum

trip time (Ttripmin) is also maintained as it corresponds to Mmax and is associated with

fault condition without PV penetration. In Table 4.12, it can be noted that the time to

trip (Ttrip) increases with increasing PV penetration for fault at the same location. To

obtain same trip time, the CTS trip parameters A, B and ρ have to be redefined or

redesigned as part of the future work. The trip times (also relay pick up times) in

Table 4.13 are consistent, where time to trip increases with fault distance.

Table 4.11: Trip times at different PV levels

Trip Times

(s)

Fault at different PV Levels at Location 2

0% 28% 33% 44% 50%

MComp 2.6500 2.4938 2.2594 2.0875 1.9781

Ttripmax 18.512 18.512 18.512 18.512 18.512

Ttrip 2.7311 2.9068 3.2481 3.5878 3.8639

Ttripmin 2.6400 2.6400 2.6400 2.6400 2.6400

Table 4.12: Trip times at different fault distance

Trip Times

(s)

Fault at different Locations for 28% PV

1 2 3 4

MComp 2.6031 2.4938 2.4000 2.3063

Ttripmax 18.512 18.512 18.512 18.512

Trip 2.7804 2.9068 3.0307 3.3430

Ttripmin 2.6400 2.6400 2.6400 2.6400


By selecting appropriate TDS values faster or slower operating time can be

selected as illustrated in Figure 4.25.

Figure 4.25: ITOC relay curves at various TDS values

4.8.3 Cases with Lower Increase in Current Magnitude

In Table 4.6, some cases of fault conditions under increased PV penetration

resulted in the increase in fault current magnitudes falling below the Mpickup when

equation (4.73) was applied. These cases include, SLF at 50% PV penetration, LLG

at 50%, 3-Ph at 44% and 3-Ph at 50% with respective increases in fault current

magnitude of 1.1042, 0.9346, 0.9527 and 0.8003. The application of equation (4.61)

to these cases gives the following Iinc values, 4.8889, 4.2222, 4.0 and 3.6667

respectively. The condition for declaring a fault and initiating the ITOC relay

sequence for trip time is Iinc > Mpickup, and for all cases this condition has been

satisfied. Applying the procedure defined in (4.74) -(4.75) resulted in the following

MComp values; 1.9781, 1.8844, 1.9313 and 1.8063 respectively for each case. These

values must fall within the range 1.15 < MComp < 2.74375, hence can be

accommodated on the selected ITOC relay curve.

4.9 CONCLUSION

In analysing the effect of PV penetration on feeder current magnitude,

particularly the fault current contribution by the substation source, a theoretical

method was established in terms of two factors, the distance to PV system and the

distance to fault as ratios of the total feeder distance. It was shown analytically

through the derived relationship that increased PV penetration will result in increased


fault current contribution from the PV systems, and consequently reducing the fault

current contribution from the substation source. Moreover, it was shown that with

increased PV penetration, and fault occurring at distance along the feeder length will

result in reduced Thevenin impedance seen at the point of fault allowing high fault

current contribution by the PV systems. The implication in terms of feeder OC

protection is that increased PV penetration will affect the fixed OC protection pickup

setting of the relays and ACRs thus affecting the protection coordination. The

theoretical analysis was verified through simulations. The proposed feeder OC

protection strategy using the DOCAS algorithm includes other features including

DC-offset suppression and fault current magnitude estimation. A fast DC-offset

suppression technique requiring no parameter estimation was achieved through

subtraction of the fault current and the average MMF output. This method incurred

no additional computational overhead as the process is an integrated part of the

algorithm in fault detection and imposes no additional delay in fault detection. A

feature for fault current magnitude estimation through a RLSE filter is incorporated

in the fault detection and diagnostic tool. The performance and functional attributes

of these features were verified through simulations and were seen to have performed

as expected without compromising the overall feeder OC protection using the

DOCAS algorithm.

The reduction in the fault current magnitude contribution by the feeder

substation source was analysed through simulation of fault conditions including

SLG, DLG, and 3-Ph faults under different PV penetration levels at fixed fault

location (FP2). It was observed that, with increased PV penetration, the fault current

contribution from the substation source reduced accordingly. Further simulations

involving the same fault conditions, at a fixed PV level (28%) and different fault

distance along the feeder length were simulated. It was observed that as fault

distance increased, the fault current magnitude seen at the feeder substation

decreased. Moreover, with PV penetration at 28%, it was observed that the fault

current contribution by the substation source was lower for the same fault conditions

at the same fault location for the case without PV penetration. This shows that the

relay will under reach faster with PV penetration and fault occurring at distance

further from the substation.

The maximum fault current would occur at a point nearest to the substation

without any PV penetration as demonstrated by the simulation results. All fault


currents; for faults along the feeder length either with or without PV will be less than

this current. The trends in the reduction of the fault current magnitude were verified

through simulation of faults at locations along the feeder and measuring the fault

current magnitudes at feeder substation. Moreover, the trends in the reduced fault

current magnitude were established through developing relationship as a ratio of the

maximum fault current measured without PV penetration.

While it was observed that fault current contribution from the feeder substation

source decreased with increased PV penetration and fault distance, a different trend

was observed when computing the actual percentage increase in percentage rather

than the actual magnitude using the concept of dynamic memory update. The MFD

value of the tall edge spike in the memory is used to calculate the percentage/per unit

increase. It must be reiterated that the MFD value in the memory before the fault is

used to determine the increase, and only updated when the fault is cleared. It was

observed through this analysis that the percentage increase in fault current magnitude

was significantly higher as compared to increase in the actual fault current magnitude

for each type of fault. Based on this observation, a strategy for declaring an OC fault

using the percentage increase in fault current magnitude in conjunction with the

adaptive feeder OC protection threshold (pickup) parameter was proposed. The

adaptiveness and scalability of the strategy was demonstrated by applying inverse-

time overcurrent (ITOC) protection strategy. A minimum time delay of ¾ cycles + 1

sample which is defined as Tdmax = 15.3125 ms is required to declare a OC fault

before the ITOC relaying is initiated to determine the trip time. The proposed method

showed that irrespective of the fault location under any PV penetration level, the

relay threshold (pickup) value was always maintained at the same pickup value.

Furthermore, it was observed that protection coordination can be achieved at

different PV penetration level by appropriately selecting relay curves at different

TDS values.

Chapter 5: HIF Detection and Classification in Distribution Feeders 129

Chapter 5: HIF Detection and Classification in

Distribution Feeders

5.1 INTRODUCTION

High Impedance Faults are common in MV and LV distribution networks, and

often are quite difficult to detect. The distribution network feeder protection system is

contingent on detection of fault current magnitude surpassing the OC pickup

threshold setting on the feeder OC relay. The HIFs generally result in much lower

current magnitude which makes the OC protection system ineffective against such

faults. From the review it was concluded that, while the techniques proposed by

researchers contribute to the knowledge towards developing a universal HIF detection

scheme, there is scope for more research. The existing OC protection system

technology has serious limitations in securely detecting and identifying HIFs. This is

due to the fact that, HIFs are complex and highly random. HIF detection technique

based on just one feature of HIF will not be applicable in another as no two HIF cases

exhibit the same characteristics. As more research continues, combination of different

techniques and methods could evolve into the development of a universal system for

reliable HIF detection and classification. When HIFs remain uncleared, the risk to

public safety and fire hazard increase.

In this chapter, the application of DOCAS algorithm in HIF detection and

classification using several classifiers using the MFD output signal is demonstrated.

Cases of HIFs were simulated using the IEEE 13 bus test system. Moreover, different

contact surfaces were simulated by randomly changing the effective resistance and

the voltage and current signal were recorded. Moreover, non HIF conditions were

simulated to differentiate the non-HIF characteristics from the HIF characteristics.

The DOCAS algorithm was used to analyse these cases to verify the effectiveness of

the DOCAS algorithm in HIF feature extraction and classification based on the MFD

output signal. A HIF detection and classification technique based on two HIF

characteristics, namely the randomness and the HIF arc extinction and re-ignition

characteristics is presented.

In section 5.2, the proposed the HIF detection and classification technique

presented. In section 5.3, describes the process in the application of the DOCAS

130 Chapter 5: HIF Detection and Classification in Distribution Feeders

MDF output in HIF feature extraction. In section 5.4 simulation and discussions are

presented followed by conclusion in section 5.6.

5.2 PROPOSED METHOD FOR HIF DETECTION AND CLASSIFICATION

The structure of the proposed algorithm for HIF detection and classification

developed from this research is shown in Figure 5.1. Notably, the block diagram

structure of Figure 5.1 is the DOCAS MFD algorithm.

The mathematical derivations and the operation attributes of this algorithm

were discussed in detail in Chapter 3, therefore no mathematical derivations are

provided in this chapter. This chapter discuss the application of the DOCAS MFD

algorithm for HIF detection and classification. The DOCAS MFD algorithm is a

multistage Morphological filter, constructed from two nonlinear MM filters called

the morphological median filter (MMF) and the Alternating Sequential Filters (ASF).

As was previously discussed, MM is a nonlinear image/signal processing technique

that analyses the topography of the input signals waveforms by means of a probing

signal called the structuring element (SE) in complete time domain. The SE is the

filtering signal that provides general functional attributes of the MM signal

processing technique. MM can detect seemingly insignificant changes in the

topography of the signal waveform being investigated, thus making it convenient for

the detection and classification of HIFs.

The DOCAS MFD algorithm extract features from the HIF current to detect

and classify HIFs based on two HIF identifying characteristics, 1) randomness, due

to randomly changing the effective fault resistance, Rf resulting in erratic fault

current waveform and, 2) arc extinction and re-ignition of the AC HIF arc around the

fundamental period resulting in a shoulder shaped unsymmetrical fault current

waveform.

Figure 5.1: Structure of the Morphological HIF detector

Open-close

ASF

Close-open

ASF ∑○

●

○

∑●

●

○+-

MFD

Output

Close

Open

MMF+-

Sampled Fault

Signal Input

f (i,v)

Initial Fault Detection

Av

MMF

fΔf

A1 A2

B1 B2

B1 B2


5.2.1 Partitions of MFD Output Signal for High Impedance Fault Detection and

Classification

The HIF detection and classification technique developed in this research

generates spikes, which are also referred to as MFD outputs or MFD values that are

relative to the slope of the transients that generate those spikes. The MFD output is

fundamental to HIF detection and feature extraction. The MFD output is defined in

terms of fault detection windows, and these serve the purpose of capturing transient

information generated by the disturbances. During faults, transients occur, and the

MFD algorithm generates spikes in response to the transient that grow inside the

fault windows within the time duration defined by twin. These spikes are used for

classifying the fault based on the nature and duration of the spikes as they span the

fault windows. For HIF detection, the MFD fault windows are partition into zones as

shown in Figure.5.2 to extract information based on the appearance of the spikes that

correlate to the targeted HIF characteristics.

The HIF detection and classification technique targets two HIF features as

stated. The regions defined as MFDTall Edge Spikes, MFDShort Edge Spikes and

MFDWindow Spikes target the randomness characteristic. Random height variation in

the edge spikes and random growth in window spikes spanning the duration of the

HIF indicate the random behaviour of HIFs.

The HIF AC arc extinction and re-ignition normally happens around the zero-

crossing of the fundamental frequency, thus the region indicated as MFDArc Spikes

targets the growth and variation in MFD spikes that correlates to the HIF arc

extinction and re-ignition characteristic.

Figure 5.2: MFD fault windows partitions for HIF detection


The HIF detection and classification technique targets two HIF features as

stated. The regions defined as MFDTall Edge Spikes, MFDShort Edge Spikes and

MFDWindow Spikes target the randomness characteristic. Random height variation in

the edge spikes and random growth in window spikes spanning the duration of the

HIF indicate the random behaviour of HIFs.

The HIF AC arc extinction and re-ignition normally happens around the zero-

crossing of the fundamental frequency, thus the region indicated as MFDArc Spikes

targets the growth and variation in MFD spikes that correlates to the HIF arc

extinction and re-ignition characteristic.

5.3 APPLICATION OF THE MFD OUTPUT IN DETECTION AND

CLASSIFICATION OF HIF

The DOCAS algorithm continuously monitors the network condition by testing

the current signal against a threshold metric. The threshold metric has minimum and

maximum, IThresholdmin and IThresholdmax respectively. If the increase in current

magnitude, denoted Iinc is such that, Iinc > IThresholdmin, a disturbance detection (DD)

flag is activated to alert the algorithm of an abnormal condition. Iinc is further tested

against the IThreshold such that, if Iinc > IThresholdmax, then level 1, (L1) flag is activated to

indicated overcurrent (OC) fault, and OC protection system is activated to act on this

fault. On the other hand, if Iinc < IThresholdmax, the level 2, (L2) flag is activated to

initiate the HIF detection and classification procedure. The sequence of operation

and description of each classification processes in the algorithm in HIF detection is

describe by the flowchart in Figure 5.3

5.3.1 Threshold Classifier

The Threshold Classifier is used to 1) detect any disturbances and 2)

discriminate between fault and normal operating conditions. A range of threshold

values for HIF detection was defined in subsection 4.6.1 of chapter 4 as 0.575 <

MThreshold < 1.15.

Referring to the flowchart, if Iinc ≤ 0.575, no disturbance exists, and the

MFDTall value in the memory is updated and the algorithm continues monitoring the

network. It must be noted that the memory update occurs every Tupdate = 9.6875 ms.

If the increase in current magnitude corresponds to Iinc > 0.575 the activate DD flag

to put the algorithm on alert that a disturbance has occurred. Then Iinc is checked

against IThresholdmax, and if Iinc > 1.15, the L1 flag is activated to initiate OC protection.


If Iinc < 1.15, then initiate the Tdmax timer and keep checking until Tdmax is timed out.

While on alert, the prefault MFDiTall value in memory is held, and subsequent

MFDTall values are tested against this value. The memory is only updated after the

alert is cancelled by means of resetting the disturbance detection (DD) flag.

It must be mentioned that the periodical updating of the MFD value in the

memory makes the Threshold parameter adaptive to changing network as well as

load conditions. Hence, the threshold parameter defined through this process can be

used in any system.

5.3.2 Timer Classifier

The Timer Classifier is defined by two time constrains denoted, Tini and Tr,

where Tini is the minimum time constrain to initiate HIF feature extraction and

classification using the MFDv signal output from the fault voltage signal input while

Tr is the reset time, also the maximum time constrain of the algorithm and occurs at

1.0s after Tini. The Timer Classifier is activated if 0.575 < IThreshold <1.15, and the

time delay from the point of fault inception to Tdmax (= 15.3125ms) defined as the

waiting time and denoted Tw must lapse before HIF classification using the MFDv

output can be initiated. The waiting time delay, Tw is used to time out any MFD

spikes generated by switching transients from common power system equipment and

loads as these spikes could be confused for spikes relating to HIF, thus, a timer is set

to allow these spikes to naturally extinguish before initiating HIF feature extraction

and classification.

The timing for the Timer Classifier, defined as Tdmax = (3N +1)∆T = 15.3125

ms. Thus, the total time delay in HIF detection from the time of point inception is Tw

+ Tr = 15.3125 ms + 1.0s = 1.0153125s

5.3.3 HIF Classification Using Feature Extraction Using MFD Output Signal

The L2 flag is activated to initiate the HIF feature extraction using the MFD

output from the voltage signal input denoted MFDv after Tdmax timer times out. The

targeted HIF feature characteristics are the randomness and HIF AC arc extinction

and re-ignition resulting in the shoulder-shaped unsymmetrical fault current

waveform. These HIF characteristics cause variation MFD value of the edges as well

as cause spikes to grow in specific regions of the MFD output signal.

Randomness: The randomness feature is extracted from the edge spikes;

MFDTall and MFDShort spikes as well as the random spikes in the fault windows


denoted MFDWindow respectively shown in Figure 5.2. The edge spikes randomly vary

in height due to transients closer to the zero-crossing of the fundamental cycle and

+ve and -ve peak respectively while the window spikes randomly appear within the

fault windows defined by the time delay Twin shown in Figure 3.9. The edge spikes

can be separated from the other spikes to observe the variation. The edge spikes are

defined by (5.1) and (5.2),

( )

20,..,

2 1vTall

kN TMFD k

k N T

= =

+

(5.1)

( )1,..,

1vShort

kN TMFD k

k N T

= =

+

(5.2)

The appearance of the elongated edges spikes is non-uniform and random, and

can occur at any point from k = 0 to ∞ and k = 1,..∞, while HIF persists.

The third class of MFD spikes considered in HIF randomness characteristic are

the MFDvWindow spikes. The MFDvWindow spikes are generated by those transients

occurring away from the zero-crossing and the positive and negative peak. The

MFDvWindow spikes appear randomly inside the fault windows, and unlike MFD spikes

due to transients from device switching and/or short-circuit faults, the MFDvWindow

spikes from HIF will prolong and span the entire length (in time) of the MFD output

so long as the HIF persists. The HIF feature extraction/classifier for the HIF

randomness feature shown in the flowchart targets the regions defined in Figure 5.2

to extract the randomness feature by observing the MFDvTall, MFDvShort and

MFDvWindow spikes.

HIF Arc Extinction and Re-ignition: The arc-extinction and re-ignition

feature of the AC arc due to HIF is extracted by observing the MFD output around

the region defined by Arc Spikes in Figure 5.2. The HIF feature extraction/classifier

for the HIF arc extinction and re-ignition feature shown in the flowchart targets this

region to extract the arc extinction and re-ignition feature by observing the MFDvArc

spikes. The MFDvArc spikes are naturally generated by the intersection of the current

and voltage waveform once every half cycle. If the voltage and current signal

waveforms are in phase, then this happens at zero magnitude of each signal. If they

are not in phase, then at the point of intersection, the instantaneous voltage and

current values are equal. In a 50 Hz system, this repeats every 0.01s. A spike is

generated in the MFDv output at the point of intersection that correlates to this


phenomenon. Let’s denote the point of intersection as Tθ, then the MFDvArc spikes

can be defined by (5.3);

2 0,...,v ArcMFD T kN T k= + = (5.3)

The AC arc extinguishes and re-ignites twice every have cycle. The arc

remains extinguished for a short period of time while waiting for the voltage to

regain until it reaches the restriking level to overcome the gap separation break down

voltage for arcing to happen again. This phenomenon impacts the fault voltage at

specific and fixed period defined in (5.3). The arc extinction and re-ignition thus

causes MFDvArc to grow in height when HIF occurs. The MFDvArc spikes can be

separated to observe for the arc extinction and re-ignition HIF characteristics.

5.3.4 Decision Logic

Decision logic is used to test the existence of the target HIF features. The

flowchart shows the conceptualized HIF feature classifiers for the detection of HIF.

The classifiers operate simultaneously to extract the randomness and arc extinction

and re-ignition features of the HIF from the target MFDv spikes. The HIF features

must be detected within the upper time limit constraint defined as Tr in the Timer

Classifier. A decision logic is used to test if the randomness and arc extinction and

re-ignition HIF features exist. HIF can only be declared if both features exist. The

proposed Decision Logic is based on Boolean logic and a set of if then rules. The

following rules constitute the decision logic:

Rule 1: if randomness; {MFDvTall AND MFDvShort AND MFDvWindow} AND arc

extinction/re-ignition{MFDvArc} are present, then initiate HIF alarm.

Rule 2: elseif randomness; {MFDvTall AND MFDvWindow} AND arc extinction/re-

ignition{MFDvArc} are present, then initiate HIF alarm.

Rule 3: elseif randomness; {MFDvShort AND MFDvWindow} AND arc extinction/re-

ignition{MFDvArc} are present, then initiate HIF alarm.

Rule 4: elseif no HIF alarm, then reset, Tr, DD flag and L2 flag.

HIF is only declared if both features are present. The condition for HIF must be

detected and declared within the time limits, thus it takes 1.0153125s from the point

of fault inception to reach a decision if HIF exists or not


Figure 5.3: Flowchart of Proposed HIF Detection and Declaration

5.4 SIMULATIONS AND DISCUSSIONS

The IEEE Power Systems Relaying Committee recommends HIF studies to be

conducted at voltage levels at 15kV or below. Test and performance verification of

the proposed algorithm were conducted through simulation studies using the IEEE 13

bus test system. The IEEE 13 bus system shown in Figure 5.4 is a heavily loaded

unbalance system operating at 4.14kV. Different software applications for power

system modelling and simulations with varied assumptions for load and line models,

and iterative computational algorithms are available nowadays [180]. Thus, it was

necessary to develop test systems for benchmarking, and making comparative

analysis of the results of the different software. IEEE recommended several

categories of test systems for power system modelling, and one category of such

systems is the unbalanced distribution system consisting of the IEEE 13.


5.4.1 Characteristics of the Simulation Test System

The details of the transformer, the complete bus data including load buses, the

load types and line parameters have been obtained from the IEEE power and energy

society (PES) website http://sites.ieee.org/pes-testfeeders/resources.

Several cases of common power system loads including capacitor and

induction motor switching, and cases of HIFs were simulated at different points on

the test system. The line classifications 602, 603 and 605 refer to the configuration of

the line segments between nodes as given Figure 5.4 with details given in the IEEE

13 bus data available on the website and will be referred to as lines in the

simulations. The voltage and current measurements have been taken at the substation

via the secondary windings of the VT and the CT respectively. The efficiency of the

CT and VT have not been modelled and included in the simulations. However, it is

assumed that the VT and CT have similar saturation characteristics and operate

within their linear region as the fault current is very small, and both having turns

ratios of 1:500. This property is accounted for in the algorithm by diving each

quantity by a factor of 500.

Figure 5.4: IEEE 13 bus test system

5.4.2 High Impedance Fault Model

The HIF model used in the simulation is shown in Figure 5.5. A line voltage of

4.16kV on the LV side represented by Vph is connected between the faulted phase and

ground. The DC voltage sources, Vp and Vn connected in series with respective diodes model

the arc voltage and have unequal magnitudes. The resistance Rp and Rn represent the arc

resistance and vary randomly to model the arcing phenomena of HIF giving an erratic

unsymmetrical positive and negative half cycle of the fault current.

http://sites.ieee.org/pes-testfeeders/resources


Figure 5.5: Emanuel Arc model in HIF simulation

Contact Surfaces: In the HIF model used, the parameters Vp and Vn model the

contact surfaces. It was experimentally shown by Emanuel et al [56] that, during

HIF, the HIF current is asymmetrical with the positive half cycle having higher value

than the negative half cycle[181]. Hence, to model this phenomenon, Vn must be

greater than Vp (Vn > Vp), and Vn – Vp = ΔV, where ΔV is unsymmetrical voltage.

Moreover, it was shown that, less densely packed contact surface (soil) yield higher

arc voltage than contact surface with high density. Using this as the guide, contact

surfaces in[182] were modelled to obtain the specified current magnitudes.

Furthermore, the values for Rp and Rn are specified in Table 5.1. These parameters

randomly vary between +10% of the specified steady state values and represent the

effective fault resistance for positive and negative half cycles respectively.

Moreover, the rate of variation of the resistance used for the respective surfaces are

40ΔT, 35ΔT, 30ΔT, 20ΔT, 15ΔT and 10ΔT going from contact surface 1 to 6. The

unequal positive and negative arc voltages and the randomly changing arc resistances

generate nonlinear V-I characteristics in each of the contact surfaces. This

phenomenon is graphically illustrated in Figure 5.6 where the V-I characteristic

curves for each contact surface are shown.

Table 5.1: Contact surfaces in HIF simulations

Contact

Surface Vp (V) Vn (A) ΔV (V) Rp = Rn IHIF (A)

1.Wet sand 750 900 150 138 ±10% 15

2.Dry sod 1000 1175 175 98 ±10% 20

3.Dry grass 1200 1400 200 70 ±10% 25

4.Wet sod 1300 1550 250 43 ±10% 40

5.Wet grass 1400 1750 350 33 ±10% 50

6.Concrete 1500 2000 500 23 ±10% 75


Figure 5.6: V-I characteristic curves of the simulated contact surfaces

5.4.3 Results of Simulation Case Studies

Simulation cases were performed to demonstrate the different classification

processes illustrated in the flowchart in Figure 5.3 for HIF detection.

Threshold Classification: Several power system conditions, including 100kVar

capacitor switching, 100 kW induction motor switching, 50 kW step load increase

and a case of single line-to-ground (SLG) fault were simulated at various points on

the test system of Figure 5.4 at different inception time, Ts. The results of increase in

fault current magnitude and the existence of the window spikes for each condition

are tabulated in Table 5.2. The increase in fault current magnitude was computed

using the process described in section 5.3.1 using the MFDiTall edge spikes from the

current signal input.

The prefault MFDiTall values used in computing the increases for each phase

are; 0.1111, 0.071 and 0.0901respectively for phases A, B and C. Note that, in three-

phase switching cases, the values in Table 5.2 are phase A results.

The results of the increase in fault current magnitude show that, SLG sustained

the highest increase of Iinc >1.15. This is an overcurrent fault, and hence, HIF

detection mechanism will not be triggered. The alert will be removed when the fault

is cleared by the OC protection mechanism. All other conditions resulted in Iinc

<1.15 which shows that there are not OC conditions. However, the following

conditions including, capacitor switching at node 611 on phase A, node 646 on phase

B, node 652 on phase C, all cases of induction motor switching and three-phase step

load increase at node 675 resulted in Iinc > 0.575 which would activate the DD flag.

The waiting time in the Threshold Classifier defined by Tdmax must time out before

HIF classification can be initiated. The time duration denoted Tdw is the time delay of

the existence of the MDFvWindow spikes for each condition as shown in Table 5.2. It


can be noted from these results that the time duration for the MFDvWindow spikes for

each case does not surpass Tdmax = 15.3125 ms which means all MFDvWindow spikes

will be extinguished by the time HIF classification using the MFDv is initiated.

Moreover, the non-existence of the MFDvWindow spikes will not satisfy any rule for the

detection of HIF based on the Decision Logic Rules in Subsection 5.3.4. Therefore,

the disturbances detected are not HIFs.

Table 5.2: Time duration for existence of the MFD spikes due to non HIF transients

Event Node Phase Ts (s) MFDiTall Edge MFDvWindow

Value IInc (pu) Tdw (ms)

SLG 632 A 0.030 0.4066 1.8299

- 0.4279 1.9257

100kVar

cap

switching

611 A 0.030 0.1364 0.6058

4.6875 0.1111 0.5000

646 B 0.035 0.0821 0.5782

9.6875 0.0721 0.5077

652 C 0.037 0.1110 0.6160

12.031 0.0901 0.5000

675 ABC 0.033 0.1212 0.5455

7.0312 0.1111 0.5000

100 kW

IM

switching

632 ABC 0.035 0.1936 0.8713

9.6875 0.1942 0.8740

675 ABC 0.037 0.1942 0.8740

7.0313 0.1950 0.8776

50 kW step

Load Inc.

645 AB 0.037 0.1184 0.5329

2.4375 0.1179 0.5306

675 ABC 0.030 0.1328 0.5977

2.4375 0.1313 0.5910

In Figures 5.7 – 5.10, graphical illustrations of the existence of the MFDvWindow

spikes for a case of SLG fault, capacitor switching, induction motor switching, and

step load increase are respectively shown. It can be observed that, in each of these

cases, the MFDvWindow spikes do not surpass Tdmax time limit. The longest time

duration of 12.031s is for the MFDvWindow spikes for capacitor switching which exists

for about two fault windows translating to half a cycle. Thus, the Time Classifier

ensures that, any non HIF related MFDvWindow spikes are naturally extinguished

within the time delay Tw = Tdmax


Figure 5.7: MFDvWindow spikes for SLG fault

Figure 5.8: MFDvWindow spikes for capacitor switching

Figure 5.9: MFDvWindow spikes for induction motor switching

Figure 5.10: MFDvWindow spikes for step load increase

HIF Randomness and Arc extinction and re-ignition Features

Classification: Several cases of HIFs on different contact surfaces defined in Table

5.1 were simulated at various locations (Lines) on the test system at different fault

inception time (Ts) to test the Randomness and the Arc extinction and re-ignition HIF

features extraction/classifications. In all cases, the Threshold Classification to initiate


HIF feature extraction/classification was satisfied by the MFDiTall spikes shown in

Table 5.3 where the first MFDiTall spikes in each case indicates the occurrence of HIF

at the point of impact. Further results from these simulations are also tabulated in

Table 5.3 which shows the time of appearance of MFDvTall, MFDvShort and MFDvArc

spikes of the faulted phases that went above the prefault value of each category of the

MFDv spikes. The prefault MFDvTall, MFDvShort, and MFDvArc values for phases (Ph)

A, B and C are respectively listed below. In the case of the MFDvTall and MFDvShort

edge spikes, the values represent the higher of the two tall edge and two short edge

spikes defined in (5.2) and (5.3) respectively.

MFDvTall : 0.4951, 0.5327, 0.4027

MFDvShort: 0.1512, 0.0633, 0.3035

MFDvArc: 0.01090, 0.01030, 0.0109

It must be noted that, Table 5.3 only contains the time of appearance of the first

seven MFDv spikes that went above the lower threshold value within the upper time

constrain (Tr). It must be stated that, there is no Counter Classification to determine

the minimum number of MFDv spikes to determine if HIF exists. The first seven

MFDv spikes are recorded to show the random time sequence of appearance of the

spikes. Moreover, any assumptions of HIF detection for each test scenario are based

on the recorded data in Table 5.3.

It was noted during simulation that in all cases, the random MFDvWindow spikes

appeared and sustained for the duration of the HIF. As defined, the random

appearance of the MFDvTall, MFDvShort and sustained appearance of MFDvWindow

spikes indicate the HIF randomness characteristic. The randomness is signified by

the non-uniform time of appearance of the MFDvTall and MFDvShort spikes. The non-

uniform time sequence can be easily observed in the time data recorded in Table 5.3

for all cases. The HIF arc extinction and re-ignition characteristic is signified by the

MFDvArc, and it can be observed that in all cases, the arc extinction and re-ignition

characteristic is consistently present, and exists when arcing occurs.

The results in Table 5.3 must be tested case by case against the Decision Logic

to determine if the HIF condition has been detected. Referring to the Decision Logic

in Subsection 5.3.4, all cases except HIF phase C of line 605 contacting with dry

grass surface has not been detected based on the first seven random MFDv spikes.

While dispersed sustained MFDvWindow spikes were observed in this case, however,


these by themselves with MFDvArc do not satisfy any of the requirements for

declaring HIF. The following can be observed from the result in Table 5.3. Contact

surfaces resulting in higher HIF current generally resulted in more transients with the

inception of HIF hence generating more random MFDv spikes while those contacting

surfaces allowing lower HIF current generated less to almost not random MFDvTall

spikes. This is visible in the results for wet sand where no MFDvTall spikes are shown.

Moreover, HIF cases closer to the point of v and i measurement (feeder substation)

gave better random MFD output as opposed to those faults simulated at locations

away from the feeder substation.

The HIF case at line 605 is such a case where the point of fault is further away

from the substation resulting in the HIF condition not being detected. Furthermore,

the dry grass contact surface is overdamped, resulting in almost no transients to

generate the necessary MFD spikes for HIF detection and classification.

In order to make these observations, and record the data, MFDvTall, MFDvShort

and MFDvArc spikes defined in (5.1), (5.2) and (5.3) respectively were extracted.

Moreover, by separating these spikes, the random MFDvWindow spikes are extracted as

well. The target regions on the MFDv output for the extraction of HIF randomness

and arc extinction and re-ignition characteristics are specified in Figure 5.2.

Extraction of Features for HIF Randomness Characteristic: The random

variations in heights of the MFDvTall, and MFDvShort spikes and the random

appearance of the MFDvWindow spikes are observed in the regions defined as, Tall

Edge Spikes, Short Edge Spikes and Win Spikes in Figure 5.2 respectively for the

randomness characteristic. Equation (5.1) shows the sequence of appearance of the

two consecutive MFDTall spike. These spikes are separated by ∆T and repeat at an

interval of 2N∆T. In equation (5.2), the sequence of appearance of the two

consecutive MFDShort edge spike is shown where these spikes are separated by ∆T

and repeat at an interval of 2N∆T as well. The MFDvTall edge spikes and MFDvShort

edge spikes are separated by time delay of Twin = 4.6875ms.

The process in extracting the randomness and HIF arc extinction and re-

ignition features is graphically illustrated through Figures 5.11-5.14 and 5.15

respectively for HIF on concrete surface at line 602 on the IEEE 13 bus feeder. In

Figure 5.11 (a) the HIF current signal and the faulted phase voltage with MFDv

output shown in Figure 5.11(b). Figures 5.12 (a), (b), (c) and (d) show respectively


the MFDvTall (1) spikes, the pu increase in MFDvTall(1) spikes, MFDvTall(2) spikes and

the pu increase in MFDvTall(2) spikes. In can be observed in Figure 5.12(b) that none

of the MFDvTall(1) spikes rose above the MFDvTall threshold limit, hence the flat line

at zero on Figure 5.12(b). However in Figure 5.12(d) it can be seen that several

MFDvTall(2) spikes went above the MFDvTall threshold which indicate the presence of

the HIF randomness characteristic.

The randomness characteristic is also extracted from the MFDvShort spikes and

the MFDvWindow spikes. Figures 5.13 (a), (b), (c) and (d) show respectively the

MFDvShort(1), pu increase in MFDvShort(1), MFDvShort(2), and the pu increase in

MFDvShort(2), edge spikes. It can be observed that the MFDvShort spikes show

characteristics of HIF randomness with random appearance of spikes with random

height above the MFDvShort threshold limit as shown in Figures 5.13(b) and (d)

respectively for the MFDvShort(1) and MFDvShort(2) spikes. Further randomness

characteristic is also extracted from Figure 5.14 which shows the sustained random

appearance of the MFDvWindow spikes after the onslaught of HIF at Ts = 0.37s.

The targeted MFDv spikes must be scanned over entire time duration defined

by Tr and applying threshold classification to determine if HIF randomness feature is

present or not.


Ta

ble

5.3

:HIF

Det

ecti

on

an

d T

ime

of

Appea

rance

of

MF

Dv sp

ikes

for

HIF

Fea

ture

Ext

ract

ion

Co

nt.

surf

ace

Lin

e

Seg

./C

on

fig.

Ph

Ts

(s)

MF

DiT

all

Spik

es

MF

DiT

all

Inc.

(pu)

MF

Dv

Spik

es

Tim

e o

f A

pp

eara

nce

of

Ran

do

m M

FD

v S

pik

es

1

2

3

4

5

6

7

Wet

san

d

(1)

63

2-6

33

(60

2)

A

0.3

7

1

0.1

2781

0.5

752

MF

DT

all

- -

- -

- -

-

2

0.1

1190

0.5

036

MF

DS

ho

rt

0.7

147

0.7

64

7

0.9

64

7

- -

- -

M

FD

Arc

0.3

984

0.4

18

4

0.4

38

4

0.4

58

4

0.5

38

4

0.6

98

4

-

Dry

sod

(2)

63

2-6

45

(60

3)

B

0.3

5

1

0.0

8165

0.5

750

MF

DT

all

0.3

947

0.4

24

7

0.4

94

7

- -

- -

2

0.0

7504

0.5

285

MF

DS

ho

rt

0.3

797

0.4

09

7

0.4

39

7

0.4

79

7

- -

-

M

FD

Arc

0.3

663

0.3

86

3

0.4

06

3

0.4

26

3

0.4

46

3

0.4

86

3

0.5

06

3

Dry

gra

ss

(3)

68

4-6

11

(60

5)

C

0.3

5

1

0.1

0369

0.5

754

MF

DT

all

- -

- -

- -

-

2

0.0

9098

0.5

049

MF

DS

ho

rt

- -

- -

- -

-

M

FD

Arc

0.3

784

0.3

98

4

0.4

18

4

0.4

38

4

0.4

68

4

0.4

98

4

0.5

18

4

Wet

sod

(4)

63

2-6

33

(60

2)

A

0.3

7

1

0.1

2770

0.5

751

MF

DT

all

- -

- -

- -

-

2

0.1

1110

0.5

000

MF

DS

ho

rt

0.4

447

0.4

64

7

0.7

47

7

0.8

64

7

0.9

44

7

- -

M

FD

Arc

0.3

984

0.4

18

4

0.4

38

4

0.4

48

4

0.4

68

4

0.4

78

4

0.4

88

4

Wet

gra

ss

(5)

68

4-6

11

(60

5)

C

0.3

0

1

0.1

0364

0.5

752

MF

DT

all

- -

- -

- -

-

2

0.0

9103

0.5

052

MF

DS

ho

rt

0.3

250

0.3

45

0

0.3

75

0

0.4

05

0

0.4

25

0

- -

M

FD

Arc

0.3

784

0.3

98

4

0.4

08

4

0.4

18

4

0.4

48

4

0.4

58

4

0.4

78

4

Co

nc.

(6)

632-6

33

(60

2)

A

0.3

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1

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DT

all

0.4

197

0.4

39

7

0.6

19

7

0.6

79

7

0.6

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7

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39

7

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7

2

0.1

1150

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MF

DS

ho

rt

0.3

750

0.3

85

0

0.3

95

0

0.4

15

0

0.4

25

0

0.4

35

0

0.4

45

0

M

FD

Arc

0.3

884

0.4

08

4

0.4

18

4

0.4

28

4

0.4

48

4

0.4

68

4

0.4

88

4


Figure 5.11: Signals for HIF at 602 on conc. surface (a) fault voltage and HIF current, and

(b)MFDv output

Figure 5.12: MFDvTall edge spikes for HIF at 602 on conc. surface (a) MFDvTall (1), (b)

MFDvTall(1) pu increase,(c)MFDvTall(2) and (d) MFDvTall(2) pu increase.


Figure 5.13: MFDvShort edge spikes for HIF at 602 on conc. surface (a) MFDvShort (1), (b)

MFDvShort(1) pu increase,(c)MFDvShort(2) and (d) MFDvShort(2) pu increase.

Figure 5.14: Random MFDvWindow spikes for HIF at 602 on conc. surface

Extraction of Features for HIF Arc Extinction and Re-ignition

Characteristic: The random variations in heights of the MFDvArc, spikes are observed

in the regions defined as, Arc Spikes for the arc extinction and re-ignition

characteristic. The sequence of appearance of the MFDvArc spikes is defined in (5.3).

The parameter Tθ represents the point of intersection in the current and voltage

waveforms, and under the network and load conditions in which the simulations were

conducted, the following time delays, is TθA = 0.0084375s, TθB = 0.00625s and

TθC = 0.0084375s representing Tθ from T0 = 0s were recorded for phases A, B and C

respectively.

The arc extinction and re-ignition feature can be visual in Figure 5.15 where

the MFDvArc spikes are shown. These spikes will also randomly increase in height

due to the HIF AC arc extinction and re-ignition about the zero-crossing of the

fundamental cycle. The random increase in height of the MFDvArc signify the

occurrence of arcing, and must be observed for the duration of Tr, then determine by

means of the decision logic if both the disturbance is HIF. In Figure 5.15 (b), the

increase in MFDvArc spikes going above the MFDvArc threshold limit are shown. In all

the simulated cases, it was observed that the MFDvArc spikes were present with pu


increases above the threshold as shown in Table 5.3. However, presence of the

MFDvArc spikes alone is not sufficient to declare HIF as defined in the decision logic.

Moreover, it was also observed that, applying threshold classification at the MFDv

output feature extraction stage resulted in so the total suppression of the target MFDv

spikes for HIF feature extraction resulting in HIF not being detected as in the case of

the HIF on phase C of line segment 605 contacting with dry grass contact surface.

Through the simulated cases, it was also observed that, those contact surfaces

allowing low HIF current magnitude had low level of transients. Moreover, the

distance of fault from the point of measurement, as observed from the HIF case at

line 605 on dry grass generated fewer transients which are necessary for generating

the target MFDv spikes for HIF feature extraction

Figure 5.15: MFDvArc spikes for HIF at 602 on conc. surface, (a) MFDvArc spikes and

(b) MFDvArc spikes with pu increase.

5.4.4 Revised HIF Feature Extraction Procedure

The HIF feature extraction/classification employed in extracting the targeted

HIF randomness and arc extinction and re-ignition features relies on threshold

classification to segregate the MFDv (values) spikes that resulted in an increase of

>0.575 from the corresponding prefault MFDv values. It was observed in all the

cases that the HIF features were present, however the application of the threshold

classification suppressed the HIF features, particularly the randomness feature

resulting one of the HIF cases not being detected. Moreover, the prefault threshold

value used in the threshold classification is the higher value of the two edge spikes

defined in (5.1) and (5.2) for each case. The two edge spikes do not have the same

prefault MFDv values, thus applying threshold classification based on the higher

value of the two resulted in the fault MFDv values from the set with lower prefault

MFDv value not going above the threshold as shown in Figure 5.12(b) for the


MFDvTall(1) increase. Moreover, the minimum threshold (IThresholdmin = 0.575) has

been determined based on the current signal and has been observed to be high when

subjecting the MFDv spikes to threshold > 0.575 pu for HIF feature extraction.

Furthermore, it is considered that application of threshold classification at HIF

feature extraction stage is not necessary to determine the randomness and the arc

extinction and re-ignition HIF characteristics as the threshold classification based on

MFDi threshold classification was already applied to detect the disturbance and

qualify the disturbance for HIF feature extraction. However, in considering the

robustness of the method, threshold classification has been applied, but the threshold

limits have been redefined.

Through the simulation studies it was observed that increase in the height of

the MFD (MFDi and MFDv) spikes is not linear with respect to the increase in fault

magnitude for the MFD spikes in different regions. The MFDTall edge spikes are

impacted by disturbances occurring at about or towards the zero-crossing of the fault

current and voltage signals which can result in very minimal change in magnitude if

the condition is not a short circuit fault. The MFDShort edge spikes on the other hand

are impacted by the disturbances around the positive and negative peaks which can

result in bigger margin of magnitude deviation as compared to the MFDTall edge

spikes. The MFDArc spikes have much lower prefault value compared to the MFD

edge spikes and occur at the point of intersection of the current and voltage signals.

However, the HIF arc extinction and re-ignition impacts MFDArc spikes causing

relatively large increase in their magnitude compared to the MFD edge spikes. This

phenomenon was observed in all simulated cases as evident in the results of Table

5.3. This has led to redefining the threshold classifier values for the different MFDv

spikes. The threshold limit for HIF classification with the following noise tolerance,

6%, 12% and 15% above the prefault MFDv spikes for MFDvTall edge, MFDvShort

spikes and MFDvArc spikes respectively provided better HIF feature extraction. It

must be noted that threshold limits do not reflect effect of distance to fault, however

have been redefined based on the 15% above nominal threshold to detect any

disturbance as previously used.

Considering the defined noise tolerances limits, the pu threshold (cut-off) limit

for MFDv spikes (values) are; 0.53, 0.56 and 0.575 respectively for the MFDvTall

edge, MFDvShort edge and MFDvArc spikes. These values are used in the revised HIF

feature extraction criterion in the algorithm. Moreover, the new MFDv Threshold


values for each category of MFDv spikes have been determined for the system

without noise consideration and provide the base values when considering the

performance of the algorithm and HIF features extraction under noisy condition.

Furthermore, in determining the threshold limit, distance to fault from the point of

measurement of signals for HIF characterisation and the type of contact surfaces are

factors that influence the level of transients and the subsequent MFDv spikes and

must be taken into consideration. Contact surfaces are random and have different V-I

characteristics, thus not considered in this analysis for selecting appropriate threshold

limit for the respective MFDv spikes.

In the revised HIF feature extraction criterion, it is considered that the change

in MFDv values given in (5.4) relate to deviation (increase/decrease) from the

prefault value. The absolute value indicates by how much the MFDv deviated from

its prefault value rather than the direction of swing (increase or decrease).

Pr

Pr

vFault v efault

vInc

v efault

MFD MFDMFD

MFD

−= (5.4)

where MDvInc is the measure by how much the MFDvFault deviates from the

MFDvPrefault, where both respectively are the prefault and fault MFDv values of each

target MFDv spikes used in HIF feature extraction except the random windows

spikes. The signal for HIF extraction is then, MFDvInc = 0.5(1+MFDvInc) – threshold

or if MFDvInc value ≤ Threshold, then MFDvInc value = 0.

The prefault values for the respective MFDv spikes for each phase of the

simulation test system are shown in Table 5.4.

Table 5.4: Prefault MFDv Values

Phase Prefault MFDv values

MFDvTall (1) MFDvTall (2) MFDvShort (1) MFDvShort (2) MFDvArc

A 0.49512 0.48512 0.15125 0.14092 0.010904

B 0.53271 0.52922 0.05334 0.06334 0.010285

C 0.40273 0.40195 0.30349 0.30270 0.01090

The revised method is applied to the undetected HIF case on phase C of line

605, which is the line segment 684-611 and given as HIF case 3 in Table 5.5. The

revised threshold limits are given in the same table, and the result for this case is

graphically illustrated in Figures 5.16 – 5.19. It must be noted that the threshold

limits are lower than the level previously determined without considering the

distance to fault. While no empirical relationship is presented, the levels have been


determined based on simulations and trial to obtain the best outcome to detect HIF.

Thus, the results in Table 5.5 establishes that there is correlation between distance to

fault and threshold limit for better chance of HIF detection, such that the threshold

limit decreases with increased distance to fault. Moreover, considering the graphical

illustration (Figures 5.16 – 5.19) of the target MFDv spikes, it is obvious that the case

of HIF on phase C of line 605 contacting with dry grass meets the HIF detection

criteria based on the decision logic for HIF declaration. The MFDvTall(1) edge and

MFDvShort(1) edge spikes show the random appearances of spikes above the redefined

threshold limit. The MFDvArc spikes are consistent and show the random appearance

above the redefined threshold.

The DOCAS MFD algorithm detected all cases of HIF by detecting the random

variations in the respective MFDv spikes. However, application of threshold

classification with inappropriate threshold limit can cause the HIF to remain

undetected.

Table 5.5: Revised MFDv Threshold limits at different fault locations

HIF

Case

Location Threshold (pu)

No. of MFDv spikes above

Threshold limit

Distance MFDvTall MFDvShort MFDv

Arc MFDvTall MFDvShort MFDvArc 1 2 1 2

1

632-633

(602) 0.515 0.520 0.850 6 9 4 6 15

0.426128

2

671-684

(604) 0.509 0.515 0.820 1 8 15 1 12

0.785969

3

684-611

(605) 0.505 0.507 0.760 4 0 15 0 16

0.842786

4

671-680

(601) 0.505 0.507 0.760 8 3 16 5 18

0.946955

Location – is the faulted line segment, Distance – total distance to fault in (miles)

from point of measurement to fault location from IEEE 13 bus data sheet available at

http://sites.ieee.org/pes-testfeeders/resources


Figure 5.16: MFDvTall edge spikes for HIF at 605 on dry grass (a) MFDvTall (1), (b)

MFDvTall(1) pu increase,(c)MFDvTall(2) and (d) MFDvTall(2) pu increase.

Figure 5.17: MFDvShort edge spikes for HIF at 605 on dry grass (a) MFDvShort (1), (b)

MFDvShort (1) pu increase, (c)MFDvShort(2) and (d) MFDvShort(2) pu increase..

Figure 5.18: Random MFDvWindow spikes for HIF at 605 on dry grass


Figure 5.19: MFDvArc spikes for HIF at 605 on dry grass, (a) MFDvArc spikes and

(b) MFDvArc spikes with pu increase.

5.4.5 Noise Consideration

The DOCAS algorithm HIF feature extraction capability under noise condition

has been tested by corrupting the HIF fault current and faulted phase voltage signals

with different signal to noise ratio (SNR). The success of the technique as previously

observed relies on selecting threshold limits. The threshold limits for the different

MFDv spikes signals at the respective SNR are tabulated in Table 5.6. The values

given in the table were determined through simulations and appropriate adjustment

of the threshold to eliminate all prefault tall spikes associated with the noise.

Table 5.6: Threshold Values at Different SNR Values

SNR

(dB)

Threshold

(pu)

Number of MFDv spikes above Threshold Limit

MFDvTall edge MFDvShort edge MFDvArc

MFDvTall MFDvShort MFDvArc 1 2 1 2

-20 0.57 0.75 1.8 1 3 1 2 2

-10 0.54 0.6 1.6 0 4 2 18 5

0 0.52 0.55 1.2 15 10 9 12 5

10 0.52 0.55 0.7 12 10 3 18 20

20 0.52 0.55 0.7 14 12 3 20 20

The DOCAS algorithm MFDv output under noise condition can be graphically

illustrated by considering the case of HIF on phase A of line 602 contacting with

concrete represented by Figures 5.20 – 5.24. The HIF current and phase voltage

signals have been corrupted with white Gaussian noise with SNR of -10dB.

Figure 5.21(a) shows that none of the MFDvTall(1) edge spikes had increase

above the threshold limit. However, Figure 5.21(b) shows the appearance of MFDvTall

edge spikes above the threshold limit.

The presence of the randomness feature can be further observed in the

MFDvShort edge and the MFDvWindow spikes respectively shown in Figures 5.22 and

5.23 with the MFDvWindow spikes having sustained chaotic appearance with increased


random magnitudes. Comparing these responses to the case without noise in Figures

5.11 – 5.15, the presence of noise reduced the rate of appearance of the MFDv spikes

with magnitude increases above the threshold limit including the MFDvArc spikes in

Figure 5.24(a) and the corresponding magnitude increase in Figure 5.24(b).

However, sufficient MFDv spikes with magnitude increases above the threshold to

declare a case of HIF exist, thus this case of HIF has been successfully detected in

the presence of noise. Moreover, the results in Table 5.6 indicate that the case of HIF

was successfully detected in all cases of noise simulation. The results in Table 5.6

and the graphical illustration suggests that, in considering the threshold limits under

noise condition, the signal to noise ratio must be measured to determine the

appropriate selection of threshold limit for each MFDv spikes. The results in Table

5.5 relates to noise evaluation perform on the HIF signal for the HIF on phase A on

line 602 contacting with concrete surface. However, the results show that noise

affects the appropriate selection of threshold limit in HIF detection when applying

threshold classification. The threshold limit increases with increased noise level.

Thus, the factors to consider in selecting threshold limit in HIF detection by

means of Threshold classification are distance to fault and the signal to noise ratio.

Such that, the threshold limits for the respective MFDv spikes are a function of the

variables distance (d) and SNR, where MFDv Threshold = f(d, SNR).

For practical implementation, the distance to fault and the SNR must be

determined to automatically select the correct threshold limits.

Figure 5.20: Signals for HIF at 602 on conc. surface (a) fault voltage and HIF current, and

(b) MFDv output with noise.


Figure 5.21: MFDvTall edge spikes for HIF at 602 on conc. surface (a) MFDvTall (1), (b)

MFDvTall(1) pu increase,(c)MFDvTall(2) and (d) MFDvTall(2) pu increase with noise.

Figure 5.22: MFDvShort edge spikes for HIF at 602 on conc. surface (a) MFDvShort (1), (b)

MFDvShort(1) pu increase,(c)MFDvShort(2) and (d) MFDvShort(2) pu increase with noise.

Figure 5.23: Random MFDvWindow spikes for HIF at 602 on conc. surface with noise.


Figure 5.24: MFDvArc spikes for HIF at 602 on conc. surface, (a) MFDvArc spikes and

(b) MFDvArc spikes with pu increase with noise.

5.5 CHALLENGES IN HIF DETECTION IN THE PRESENCE OF

INCREASING RE BASED DER IN RADIAL DISTRIBUTION

NETWORKS

The increased penetration of RE based DERs such as photovoltaic (PV)

systems directly into the Medium to Low voltage radial power distribution networks

introduces new challenges to the already difficult task of developing secure

protection system including HIF detection and identification.

As was previously discussed, HIFs generally induce very small fault current

magnitude in the range of between 10A-50A RMS which causes extreme difficulty

in being detected by the OC protection systems. The increased penetration of RE

based DERs at the feeders reduce the fault current contribution from the substation

(SS) source which can exacerbate the already difficult task of designing an adaptive

protection to detect all types of fault conditions including HIFs.

The following simulation studies are conducted to demonstrate the difficulties in

HIF detection in distribution networks with RE based DERs such as PV systems

using OC protection scheme.

5.5.1 Simulation System

A sample radial distribution feeder with PVs is modelled in

MATLAB/Simulink as shown in Figure 5.25 is used to test the proposed algorithm.

All system components remain as previously described however have been

reconfigured for a system voltage of 4.16kV. The network spans 40 km with PV1

located 10 km from the substation, and the remaining PVs located 10 km apart. The

distributed loads at each PV location are at 75 kW for total feeder load of 300 kW and

are shared by the main source and the local PVs. The PVs are rated at 100 kW each


giving a total feeder capacity of 2.4 MW. All PV systems were simulated at STC

(1000 W/m2, 25°C) and the voltage and current signals are taken from the secondary

of the VT and CT respectively.

Two cases of HIFs with very slowly changing Rf (sandy contact surface) were

simulated at fault locations XF1 and XF2 for HIF at locations near and further away

from the point of measurements. Moreover, the fault locations also determine the

number of sources (SS and PV) feeding the fault.

Figure 5.25: Radial feeder with PV

5.5.2 HIF at Location XF1 - Closer to Point of Measurements

The prefault MFDiTall edge values representing the current magnitude without

and with PV penetration are shown in Table 5.7.

The prefault MFDiTall edge values measured at the SS indicates a current

reduction of 18.02% because of PV penetration. The considered case of HIF is

initiated at 0.06s. Considering the case of HIF occurring without PV penetration, the

fault current increased by 10.66%. This is much lower than the normal OC threshold

limit of more than 2 to 3 times the prefault current to detect any fault. With PV

penetration the HIF results in a fault current magnitude increase of 7.43%. The net

effect of PV penetration on fault current magnitude measured at the SS is considered

relative to the prefault current without PV penetration resulting in the decrease in

current magnitude calculated as (1 – 0.0347/0.0394) x 100 = 11.3%. This shows that

increased penetration of PV can aggravate the difficulty in HIF detection using the

OC protection scheme. Moreover, the reduced fault current magnitude reaffirms the

dynamic behavior of the feeder due to penetration of RE based DERs such as PVs.

The plots in Figures 5.26 to 5.27 show the effect of PV penetration on the current

magnitude. Figure 5.26 (a) shows the fault current waveforms without and with PV


penetration with respective MFDi outputs shown in Figures 5.26 (a) and (b). The

impact of PV penetration is observed by considering the MFDiTall edge spikes; hence

MFDiTall (1) and MFDiTall (2) edge spikes are shown in Figures 5.27 (a) and (b) to show the

trend. The reductions in the current magnitude due to the increased PV penetration are shown

in both plots. The fault location XF1 is nearer to the substation and located between PV1

and PV2. Therefore, the MFDiTall values associated with these sources increased from

their prefault values as shown in Table 5.7. PV3 and PV4 are located further away

from the fault hence they are blind to the HIF at XF1. Moreover, the fault current

magnitude is so insignificant to have any implication on these PVs.

Figure 5.26: DOCAS MFDi outputs for HIF at XF1 closer to the feeder substation (a) MFDi

without PV, (b) MFDi with PV

Figure 5.27: DOCAS MFDi outputs for HIF at XF2 further from the feeder substation (a)

MFDi without PV, (b) MFDi with PV


5.5.3 HIF at Location XF2 - Further from Point of Measurements

The prefault MFDiTall edge values representing the current magnitude without

and with PV penetration are shown in Table 5.8. The effect on the substation current

due to HIF occurring at a distance further away from the substation in a radial

distribution network with PV integration is demonstrated. The decrease in the

prefault MFDiTall for without and with PV in Table 5.8 compared to Table 5.7 is

associated with the increase in the Thevenin equivalent impedance with increased

fault distance.

Figures 5.28 to 5.35 demonstrate the impact on fault current magnitude in

radial feeders with increased RE based DERs for HIF occurring at a distance further

away from the point of signal measurements. Figures 5.28(a) shows the fault current

signal waveforms with and without PV penetration and respective MFDi outputs

shown in Figures 5.28 (b) and (c). The net effect of PV penetration on fault current

magnitude measured at the SS for the fault at XF2 from prefault value without PV to fault

with PV results in a decrease in current magnitude calculated as (1 – 0.0323/0.0370) x 100 =

12.7%.

The fault location XF2 is towards the end of the feeder and between PV3 and

PV4. The results in Table 5.8 show that the MFDiTall values for the two PV systems

increased by 10% in response to the HIF. The increased current injection by PV3 and

PV4 caused feeder current seen at the SS to drop from its prefault value of 0.0353 to

0.0323 resulting in an 8.5% drop fault in current magnitude. This phenomenon can

cause the feeder relay to see this as a load reduction. Figures 5.29 (a) and (b)

graphically illustrate this condition where the MFDiTall (1) and MFDiTall (2) edge

spikes show the decrease in fault current magnitude seen at the feeder substation.

Such a condition will aggravate the deficiency in the feeder OC protection

system rendering it totally blind to the existence of this fault condition.

The results from these simulations reaffirm the deficiency of the OC protection

system in the detection and identification of HIFs thus requiring other methods such

as the DOCAS algorithm.


Table 5.7: MFDiTall edge values for HIF at location XF2

MFDi

Parameters

MFDiTall

without PV MFDiTall with PV penetration

SS SS PV1 PV2 PV3 PV4

Prefault 0.0370 0.0353 0.005 0.009 0.010 0.010

Fault 0.0419 0.0323 0.005 0.009 0.011 0.011

Diff 0.0049 -0.0030 0 0 0.0001 0.0001

5.5.4 Application of DOCAS Algorithm in HIF Detection in Radial Distribution

Feeders with Increased Penetration of RE Based DERs

The condition defined in subsection 5.5.3, and for which the MFDiTall output for the

simulated case depicted in Figure 5.27(b) poses a serious challenge in HIF detection

using any conventional OC protection technique. HIF detection under such condition

will have to rely on feature extraction. The effectiveness of the DOCAS algorithm is

tested under this condition. For this purpose, additional HIF simulation has

performed at location XF2 under same load condition with fallen conductor on dry

grass contact surface. The fault current signals for the case of with and without PV

penetration are shown in Figure 5.28(a). The MFDi outputs of the DOCAS algorithm

for these scenarios are respectively shown in Figure 5.28(b). The trend in the

behaviour of the fault current magnitude under these scenarios is graphically

illustrated by extracting the respective MFDiTall (1) and MFDiTall (2) outputs (spikes)

as shown in Figure 5.29 (a) and (b) respectively. The HIF is initiated at 0.4s, and the

trend shows that HIF occurring without PV results in an increase in fault current

magnitude while in the case of HIF with PV penetration, the fault current magnitude

decreases. The effectiveness of the DOCAS algorithm in detecting HIF is tested

against HIF occurring at XF2 with PV penetration.

The procedure in HIF detection using the DOCAS algorithm is implemented

according to the flowchart in Figure 5.3. The first step is detection of disturbance

based on threshold classification using the MFDiTall edge spikes. The increase in

MFDiTall edge spikes is determined according to (5.5). The MFDiTall (1) and MFDiTall

(2) edge spikes with their respective pu increases above the cut-off threshold 0.575

pu are shown in Figure 5.30. The disturbance is dictated by MFDiTall (1) edge spikes

as shown in Figure 5.30(b). The MFDiTall (2) edge spikes show no increase above the

threshold as show by Figure 5.30(d). The increase in the MFDiTall (1) edge spikes fall

within 0.575 < IThreshold <1.0 which satisfies the requirement for further HIF

extraction based on DOCAS MFDv output based on the fault voltage signal input.


The fault voltage signal for the HIF with PV penetration at location XF2 is

shown in Figure 5.31 (a) with corresponding MFDv output in Figure 5.31(b).

The randomness and HIF arc extinction and re-ignition features are extracted from

the MFDv output by targeting the MFDvTall edge spikes, the MFDvShort edge spikes,

the random window, MFDvWindow spikes and the MFDvArc spikes shown in Figures

5.32 – 5.35 respectively. The following prefault MFDv values were recorded for the

respective target MFDv spikes, MFDvTall (1) =0.4921; MFDvTall (2) =0.5032;

MFDvShort (1) =0.2482; MFDvShort (2) = 0.2393 and MFDvArc = 0.00954. The

thresholds are set at 5% above prefault value the MFDvTall edges spikes, 8% for the

MFDvShort edges spikes and 10% for the MFDvArc spikes. Figure 5.32 shows that

neither of the MFDvTall edge spikes had increase above the threshold limit. In Figure

5.32(b) the MFDvShort (1) shows the randomness characteristic with spikes having

random height above the respective threshold limit. Moreover, the randomness

characteristic is also shown in the random window, MFDvWindow spikes in Figure

5.34. The window spikes randomly vary in height after HIF at 0.4s and persist.

Furthermore, prolonging spikes appear in the fault windows with random heights. In

Figure 5.35(a), the MFDvArc spikes are shown with those spikes going above

threshold with random height in Figure 5.35(b).

The requirements for HIF detection based on the decision logic have been

satisfied; hence the HIF under this condition has been detected by the DOCAS

algorithm.

Figure 5.28: HIF signals for HIF at XF2 on dry grass (a) fault currents for with and without

PV, (b) MFDi outputs for with and without PV


Figure 5.29: MFDiTall spikes for HIF at XF2 on dry grass (a) MFDiTall (1) spikes for with and

without PV, (b) MFDiTall (2) spikes for with and without PV

Figure 5.30:MFDiTall spikes and MFDiTall pu increases for HIF on dry grass at XF2 with PV

(a) MFDiTall (1) spikes, (b) MFDiTall (1) pu increase, (c) MFDiTall (2) spikes, (c) MFDiTall (2)

pu increase

Figure 5.31: HIF signals for HIF at XF2 on dry grass (a) fault voltage with PV, (b) MFDv

output


Figure 5.32: MFDvTall spikes and MFDvTall pu increases for HIF on dry grass at XF2 with PV

(a) MFDvTall (1) spikes, (b) MFDvTall (1) pu increase, (c) MFDvTall (2) spikes, (c) MFDvTall (2)

pu increase

Figure 5.33: MFDvShort spikes and MFDvShort pu increases for HIF on dry grass at XF2 with

PV (a) MFDvShort (1) spikes, (b) MFDvShort (1) pu increase, (c) MFDvShort (2) spikes, (c)

MFDvShort (2) pu increase

Figure 5.34: Window, MFDvWindow spikes


Figure 5.35: HIF arc extinction and re-ignition, MFDvArc spikes

5.6 CONCLUSION

The strategy for HIF detection and classification require feature extraction

from the DOCAS MFD output. The MFD output from current signal input,

designated MFDi and voltage signal input designated MFDv are used in the

classification process. The segregation of the non HIF related features from the HIF

features are achieved through conceptualized classifiers including Threshold

Classifier, Timer Classifier, and MFD output Feature Extraction/Classifiers including

Randomness and HIF Arc Extinction and Re-ignition Classifiers. In threshold

classification, the Threshold Classifier uses the upper and lower level threshold

values to differentiate a possible HIF related disturbance from a non HIF related

disturbance. The Time Classifier is used to time out any non HIF related MFD spikes

generated by the transients from the disturbance. The Threshold Classifier uses the

MFDi tall edge spikes while the Timer Classifier observes the fault window of the

MFDv spikes existence of random window spikes.

Simulation case studies including, a case of SLG fault, 100 kVar capacitor

switching, 100 kW induction motor switching and 50 kW step load increase were

simulated using the IEEE 13 bus test system to validate the Threshold and Timer

Classifiers. It was observed that, through these classification processes, only those

disturbances meeting the characteristics of HIF are processed. The HIFs generally

result in lower fault current, thus Threshold parameter with lower and upper

threshold limits have been defined with noise tolerance of 15% above prefault MFDi

value for both upper and lower thresholds. It was further observed that, the window

spikes from MFDv output are short lived, and die out before the Timer Classifier

limit of 15.3125 ms. The Threshold and the Timer Classifiers operate simultaneously

once a disturbance is detected. If the condition of the Threshold and Timer

Classifiers are met, further HIF classification based on feature extraction is initiated.


Several cases of HIFs at locations on IEEE 13 bus test system were simulated

on six different contact surfaces to test and verify the HIF feature

extraction/classification process. For HIF feature extraction, the MFDv is

sectionalised to observe for the target HIF features of randomness and HIF arc

extinction and re-ignition. The extracted HIF features were tested against decision

logic to determine if the condition is HIF. Through the simulation, it was observed

that the proposed method is effective when sufficient transient is generated by the

HIFs.

In all simulated cases, it was observed that the HIF arc extinction and re-

ignition characteristic was present. This feature will be present if arcing exists. The

randomness feature on the other hand depends on the rate of change of the effective

fault resistance. Highly damped (densely compact) surfaces resulted in low current

magnitude with fewer transients due to slow changing effective resistance. The

DOCAS algorithm generates MFD spikes in response to the transients. It was

observed that, in one case of HIF at line 605 contacting with dry grass there were

variations in the edge spikes however; none of the edge MFDv spikes went above the

threshold value to suggest HIF. The line 605 is further than all other test points used

in the simulation, thus is observed that distance to fault with respect to the point of

measurement of the input signal can affect the sensitivity of the algorithm. In this

case it was observed that, the random window spikes were present with arc

extinction and re-ignition spikes; however, it is not one of the rules in the decision

logic to confidently declare any case of HIF. Thus, no HIF was declared in this case.

It was observed that, in all simulated cases, the DOCAS algorithm detected the

existence of the HIF by generating MFDv spikes with randomly changing heights

(values), however segregation through thresholding with inappropriate threshold

limit for HIF feature extraction can result in the suppression of the HIF, thus

undetected as demonstrated by the case of HIF on phase C of line 605 contacting dry

grass. Thus, the threshold classification used in feature extraction using the MFDv

spikes was revised for better performance as demonstrated by considering the

undetected cases. Moreover, it was determined that, distance to fault from the point

of signal extraction is a factor to be considered in selecting the threshold limit in

feature extraction.

The performance of the DOCAS algorithm in feature extraction under noise

condition was evaluated by corrupting the fault voltage and the HIF current signals


with white Gaussian noise with different SNR. The algorithm performed successfully

under the simulated noise conditions.

It was generally observed that, while high level of noise has implications on the

performance of algorithm, the HIF features were still present but the interval of

appearance on MFD spikes that wen above the threshold became lengthened. The

implication is that, under noisy condition, the existence of cases of HIF may not be

detected within the reset time (Tr), the HIF detection delay must be extended under

noisy condition. Moreover, distance to fault with respect to the point of signal

measurement and the SNR are factors to consider in the selection of appropriate

threshold limit.

The simulation studies for HIF detection in radial distribution feeders with

increased RE based DERs revealed a potential difficulty that could be encountered,

especially in detecting HIFs occurring away from the point of measurement and

closer to larger DERs are capable of increasing their output to entirely feed the fault.

The increased output from the DERs in response to the HIF further reduces the

current contribution from the grid through the substation. The effectiveness of the

DOCAS algorithm has been tested under this scenario, and its performance has

shown remarkable outcome, which demonstrates that the DOCAS algorithm can

detect the HIF condition even under this condition.

Under all conditions and factors considered in the simulations, all cases of HIF

conditions were detected within the reset time delay of 1.0153125 s.

In considering the practical implementation of the algorithm, it is necessary to

maintain precise timing to extract the target MFDv spikes for the randomness and the

arcing characteristics of HIF. Moreover, the phase angle between the voltage and

current must be monitored to determine their point of intersection for the extraction

of the MFDvArc spikes. Thus, phasor measure units (PMU) at the point of

measurement need to be installed to extract the information.

While the DOCAS algorithm has the ability to respond to OC fault, however it

is anticipated that it could be used in tandem with the existing OC protection in

providing the mechanism for HIF detection which the existing OC protection system

lacks.

Chapter 6: DC Arc-Fault Detection in PV Systems 167

Chapter 6: DC Arc-Fault Detection in PV

Systems

6.1 INTRODUCTION

Advances in power electronics converters have seen increased levels of RE

based DERs especially PV systems in distribution networks. Moreover, the direct

integration of PV system provides opportunity for the existence of DC power system

to supply DC loads directly. Like any power system, fault can occur on any part of

the system, and must be protected to prevent damage as well injury to people.

Protection for DC power system is still in its infancy as compared to AC power

protection. The conventional overcurrent protection strategy using current magnitude

as the threshold metric is applied for all types of faults in the DC power systems

including PV systems. However not all fault conditions on the DC system can be

adequately protected using such strategy. One such fault condition is the DC Arc-

Fault occurring on the DC systems including the PV system. In this chapter, a

technique for DC arc-fault detection in PV systems based on the DOCAS algorithm

is presented. The effectiveness of the algorithm during low irradiance as well as

transition in irradiance from partial shading is tested. The robustness of the algorithm

in differentiating arcing faults from non-arcing conditions is demonstrated by

simulations. The rest of the chapter is organised as follows: In section 6.2, the

proposed DC arc-fault detection technique is presented. In section 6.3 simulations to

verify the proposed scheme are presented with conclusion in section 6.4.

6.2 THE PROPOSED DC ARC-FAULT DETECTION TECHNIQUE

The DC arc fault detection technique presented herein proposes to use current

and voltage signal extracted by placement of appropriate sensors at the PV strings

and DC bus. The measured current and voltage signals are fed as input to the DC arc

fault detector which is centrally located within the vicinity of the inverter. Presence

of arc is detected by continuously monitoring and Analyzing variation in signals

extracted at the input of the inverter and on the PV array DC bus (before the DC-DC

converter). The structure of the DC arc-fault detector is shown in Figure 6.1.

168 Chapter 6: DC Arc-Fault Detection in PV Systems

Figure 6.1: Block diagram of the DC Arc-Fault detection system

6.2.1 DC Arc Fault Detector

The DC arc-fault detector uses the DOCAS MFD algorithm to detect the

chaotic behavior of the sustained DC arc-fault. There are two functional components

to achieve the desired output, the cascaded filters and SE. The architecture of the

DOCAS algorithm is dictated by the structure of the designed SE. The SE is a

filtering signal that dissects the topography of the graph of the fault signal to extract

feature for fault detection and classification. All the SEs, A1, A2, B1 and B2 used in

DC arc fault detection are directly inherited as applied in OC fault protection and

HIF detection and classification. Moreover, the mathematical derivation and design

of the algorithm remains the same as presented Chapter 3.

6.2.2 The DOCAS MFD Signal Output in DC Arc-Fault Detection

The procedure for DC Arc-fault detection is the same as fault detection in the

AC power system. However, in the DC Arc-Fault detection, the concept of fault

detection window is not available because of the non-sinusoidal nature of the (DC)

signal. The onslaught of the arc is detected by appearance of spikes which increase in

height in response to the fast changing (rate of change of the random DC arc) noise

like DC arc characteristic that sustains when ignited. The spikes in the DOCAS MFD

output appear chaotically. The DOCAS MFD algorithm detects DC arc-fault by

means of detecting the chaotic behaviour of the random DC arc.

The fault voltage and current, Vdc and Idc are operated on at the MMF stage to

create the initial fault detection signals, ΔVdc and ΔIdc by subtracting the average

MMF output from the input signals to detect existence of DC arc-fault. The chaotic

behaviour of the DC arc is detected further at the ASF stages for the classification of

DC arc-fault.

Open-close

ASF

Close-open

ASF ∑○

●

○

∑●

●

○+-

MFD

Output

Close

Open

MMF+-

Sampled Fault

Signal Input

Idc

Initial Fault Detection

Av

MMF

ΔIdc

A1 A2

B1 B2

B1 B2

Vdc

VdcIdc

ΔVdc


6.3 SIMULATIONS AND DISCUSSIONS

To test the effectiveness of the DOCAS MFD algorithm in DC arc-fault

detection, the system configuration of Figure 6.2 was modelled in

MATLAB/Simulink.

6.3.1 Characteristics of the Simulation System

The 154 kW PV system is connected to a 4.16kV radial distribution feeder with

a substation supply capacity of 5 MW suppling distributed loads along the feeder

totalling 4.5 MW. The PV system is connected to the feeder through a 250kVA,

415V/4.16kV, 50Hz, Δ-Y transformer with grounded Y connection providing

galvanic isolation of the PV system from the AC system.

Figure 6.2: Radial distribution feeder with PV penetration used in the simulation study

PV system Configuration: The PV system in the simulation system is

modelled using the SunPower SPR -320E -WHT-D PV nodules available in the

Simulink library. The PV modules have the following specifications; Maximum

Power = 320.542 W, Open circuit voltage (Voc) = 64.8 V, Voltage at maximum

power point (Vmpp) = 54.7, Short-circuit current (Isc) = 6.24 A and Current at

maximum power point (Impp) = 5.86. The PV system is made up of four 19.233kW

parallel PV arrays each having four parallel PV strings made of fifteen series PV

modules. The configuration of the PV array used in the simulation is shown in Figure

6.3. The PV arrays arrangement for the PV system is shown in Figure 6.4.

The I-V and P-V plots for the PV string configuration at STC, (1000 w/m2,

25ºC) are shown in Figure 6.5. Based on this configuration, the PV strings operate at

maximum power point voltage (VMPP) of 820.5 DC and maximum power point

current (IMPP) of 5.86A and provide 4.81 kW per string capacity under STC. A


300W, 850V DC generic inverter is used produce 415V AC output. MPPT using

Perturb and Observe algorithm is used to regulate Vdc. A Description of the

components is given in section 4.7 of Chapter 4.

The fault conditions simulated occur in the strings in PV array 1 and

considering the nature of the fault and the level of irradiance considered, it is

assumed that only the strings within the faulted array are affected. The location and

types of faults considered in the simulation are indicated in the diagram of Figure

6.6.

Figure 6.3: PV array configuration in the simulation system

Figure 6.4: A typical PV system configuration array configuration


Figure 6.5: Characteristic curves for the PV String configuration (a) I-V curve, and

(b)P-V curve

Figure 6.6: Layout of the PV modules in the PV strings for fault simulations.

6.3.2 DC Arc-Fault Model

The DC Arc model used in the simulation to generate the DC Arc-fault current

and fault voltage is the model developed by Stokes and Oppenlander [31] given in

(6.1);

( ) 12.1534.020 arcgarc IzV += (6.1)

where Iarc = Vs/(Rs+Rarc). Vs and Rs are source voltage and resistance

respectively. The zg parameter in (6.1) and (6.2) is length of the arc gap given in

millimetres. A fixed gap distance of 20 mm was maintained, and the arc current was

injected with ±10% random variance with mean value prefault load current at

difference irradiance for faults under specified irradiance.


The following assumptions are made in these simulations and analysis. That, a

galvanic isolation between the grid and the PV system through a delta/Y, transformer

with Y grounded exists, thus zero fault impedance is implied. The fault current is

only from the PV strings within the faulted array, and no fault current and/or voltage

contribution from the inverter. Further assumptions in the simulations and

performance of the DOCAS algorithm in DC arc-fault detection are; the proposed

DC arc-fault detection method using DOCAS algorithm only detects arcing faults,

which implies the DOCAS is blind to any faults that do not initiate arcing. Under this

premise, it is anticipated that the proposed technique is to be operated in tandem with

the existing DC overcurrent (OC) protection scheme. Moreover, it is assumed that

the existing DC OC protection scheme is capable of detecting any type of fault

resulting in sufficient backfed current particularly at high irradiance. It is further

assumed that, any effective and reliable DC arc-fault detection system is capable of

detecting DC arc-fault in both grounded and ungrounded (floating) PV systems.

Moreover, it is assumed that, ungrounded fault in a grounded PV system exhibits

similar characteristics as any fault condition in an ungrounded PV system. Thus,

simulations have been performed on ungrounded system.

The innovations in the proposed DC arc-fault detection technique using the

DOCAS algorithm relies on DOCAS ability to detect insignificant changes in the DC

current and voltage signal under fault conditions. Thus, the simulations have been

conducted for arcing fault conditions under very low level of irradiance. Moreover,

DOCAS’ ability to differentiate between non-fault conditions such fast transition in

irradiance due to cloud movements and response of the MPPT algorithm have been

simulated. Irradiance levels of 50 W/m2 to 150 W/m2 were used in the simulations to

test the functional capability of DOCAS algorithm. Prefault tests at these irradiances

while maintain a constant average temperature of 30°C were conducted to determine

the current magnitude at the PV strings and the DC bus on the PV system for which

the results are tabulated in Table 6.1.

Considering the results in Table 6.1, it is apparent that load currents extracted

at various points of measurements given in Figure 6.6 are much less compared to the

load current at STC at the same points. Any fault, let alone arcing fault under these

conditions will not generate fault current above 2.1 times (11.72 A) above the load

current at STC at each point to blow out any series inserted fuses at the PV strings or

the OCPD.


Table 6.1: Prefault current measurements for PV strings at different irradiances

Irradiance

W/m2

Prefault Current (A)

PV Array PV Strings

1 2 3 4 50 0.3105 0.3105 0.3105 0.3105 1.2420

75 0.4662 0.4662 0.4662 0.4662 1.8648

100 0.6216 0.6216 0.6216 0.6216 2.4862

150 0.9325 0.9325 0.9325 0.9325 3.730

6.3.3 DC Arc-Fault Conditions at Low Irradiance

Several cases of arc faults were simulated at points indicated on Figure 6.3.

The faults are generally defined as, parallel faults, for Fp1-Fp7 while Fs1-Fs4 are

series faults. The parallel faults can be reclassified as mismatch faults resulting in

voltage mismatch in the PV strings. Faults Fp1-Fp6 are mismatch faults while Fp7

will be considered as parallel fault.

Mismatch Arcing Faults: The mismatch faults Fp1-Fp3 and Fp6 bridge two

points at different voltages on the same PV string. Fp4 and Fp5 are mismatch faults

that bridge two points at different voltages on adjacent PV strings. The severity of

the mismatch faults can be evaluated base on their mismatch percentage. The

mismatch percentage is determined based on the number of PV modules in the string

that are affected by the fault. Consider a mismatch fault on the same string such as

Fp1. This fault has three PV modules affected, thus the mismatch percentage is (3/15)

x 100 = 20%. Now consider a mismatch fault between two adjacent strings such as

Fp4 affecting PV modules on string 2 (PVS2) and string 3 ((PVS2). There are 9

affected PV modules in PVS2 and 3 in PVS3, thus the mismatch percentage is ((6-

3)/15) x 100 = 40%. The mismatch percentages for all mismatch faults are

determined following this procedure. The fault currents obtained for each simulated

fault condition are tabulated in Table 6.2.

During fault, the unfaulted strings view the faulted string as a load, hence feed

current (negative current, Ireverse) to the faulted string, where this current is the

backfed current. The PV string load current under fault is;

backfedefaultFaultLoad III −= Pr_ (6.3)

where Ibackfed is the backfed current from the adjacent unfaulted PV strings. From

(6.4), the backfed current is;


FaultLoadefaultbackfed III _Pr −= (6.4)

The backfed current calculated according to (6.4) are tabulated in Table 6.3.

The general trend in the results in Table 6.3 show that the backfed currents increase

with increase in irradiance. However, the arcing faults simulated at the low

irradiance levels still resulted in insignificant backfed currents which indicate that,

for the simulated PV system, the backfed current at each faulting PV strings will not

reach 2.1 times load current of the strings at STC. Thus, the arcing fault will persist.

Observing the results in Tables 6.2 and 6.3, it can be noted that some mismatch

faults having equal mismatch percentage do not exhibit same fault characteristics.

Compare for instance Fp2 (or Fp3) with Fp4 having 40% mismatch. This alludes to

the fact that fault location and fault resistance also dictates the level of backfed

current. The faults Fp2 and Fp3 are in parallel to the faulted PV modules, but are in

series with unfaulted modules in the same string. The resistance of each PV module

in the string is given as, RPV = v/i, where v and i of the PV module at any irradiance

and is inversely proportional to the irradiance. The fault is in parallel to mRPV where

m is the number of faulted PV modules. In arcing fault, the arc channel is represented

by Rarc which is parallel to mRPV to give effective fault resistance as (mRPV x Rarc)/

(mRPV + Rarc). Thus, fault location influences the fault resistance, and consequently

the backfed current.

Table 6.2: Load current under simulated fault conditions at different irradiances

Fault

Type

Mismatch

(%)

PV String Load Current (A) under Fault at Simulated Irradiances

(W/m2)

50 75 100 150

Fp1 20 0.2691 0.4247 0.5804 0.8378

Fp2 40 0.2484 -0.1544 -0.1872 -0.3643

Fp3 40 0.2484 -0.1554 -0.1872 -0.3643

Fp4 40 0.2070 0.3089 0.4493 0.6192

Fp5 20 0.2691 0.4247 0.5804 0.8378

Fp6 60 0.1656 -0.5019 -0.7489 -0.4371


Table 6.3: Calculated backfed current for the simulated fault conditions

Fault

Type

Mismatch

(%)

PV String Backfed Current (A) under Fault at Simulated

Irradiances (W/m2)

50 75 100 150

Fp1 20 0.0414 0.0415 0.0412 0.0947

Fp2 40 0.0621 0.6206 0.8088 1.2968

Fp3 40 0.0621 0.6206 0.8088 1.2968

Fp4 40 0.1035 0.1573 0.1723 0.3133

Fp5 20 0.0414 0.0415 0.0412 0.0947

Fp6 60 0.1449 0.9681 1.3705 1.3697

The proposed arcing fault detection method can be visualized through graphical

illustration of one of the simulated cases, where faults are initiated at 1.5s. Fault Fp1

was simulated on PV string 1 at all levels of irradiance; the graphical illustrations in

Figure 6.7 are related to irradiance of 50 W/m2. Figure 6.7 (a) shows the drop in PV

string 1 load current. Figure 6.7 (b) is the fault current transformed by the MMF

stage of the DOCAS algorithm. Figure 6.7 (c) is the difference fault current signal

for the DC arc-fault detection. In Figure 6.7 (d) is the MFD output showing the

onslaught of arcing fault at 1.5s. The algorithm detects the rate of change in the fault

current signal to generate spikes. The chaotic nature of the arc generates random

spikes that grow in height randomly to correlate to the occurrence and changes in the

arc fault current and voltage signals. Arcing is characterized by random and

persistent spikes where as in normal transients the spikes will be short lived.

Fault voltage signals in Figure 6.8 (b)-(d) illustrate the same phenomena based

on voltage signal input. Graphs in Figures 6.7 (a) 6.8 (a) are generated from signal at

the input of the inverter. It is not possible to locate the faulted string based only on

the output of the signal at the inverter input. Figure 6.9 shows the PV string fault

current signals. As it is obvious, fault, Fp1 occurs at string 1 as shown in Figure

6.9(a) where the load current drops as compared to the other unfaulted PV strings.

Figure 6.10 is the corresponding MFD out of the PV strings fault current signals.

While the MFD output of the faulted string, Figure 6.10(a) shows taller spikes

compared to the unfaulted strings, it alone cannot allude to the faulted string. Both

signals are needed to locate the faulted string.


Figure 6.7: Current signal measured at DC bus with associated signals for DC arc-fault

FP1

Figure 6.8: Voltage signal measured at DC bus with associated signals for DC arc-fault FP1

Figure 6.9: MFD outputs for PV string currents for fault DC arc-fault FP1

Parallel DC Arc-Faults: A case of parallel DC arc-fault, Fp7 at irradiance of

50 W/m2 was simulated at the input of the inverter. A graphical illustration of the


fault detection occurring at 1.5s is shown in Figures 6.11 and 6.12 for the load

current and voltage respectively. The DC current input to the inverter is;

FaultPVinvDC III −=_ (6.5)

where IPV is the sum of PV string currents and Ifault is the fault current through

the arc channel. The DC input current to the inverter drops as shown in Figure

6.11(a). Since it is an arcing fault, the presence of arc is detected by the appearance

of random and sustained spikes in the MFD output shown in Figure 6.11 (b). The

presence of the arc is also shown on the MFD output of the voltage signal in Figure

6.12 (b) as well. In Figure 6.12 (a) the initial decrease in voltage due to the fault is

shown. The MPPT quickly restore the voltage and puts the system to operate at a

lower maximum power (MPP).

The PV strings initially seem to increase their output as indicated in Figure

6.13 but due to the fast action of the MPPT, the PV strings maintain the mean load

current before the fault with sustained arc fault current generation. The

corresponding MFD outputs in Figure 6.14 also indicate the presence of the arc.

Figure 6.10:Current signal measured at DC bus and MFD out for DC arc-fault FP7

Figure 6.11: Voltage signal measured at DC bus and MFD out for DC arc-fault FP7


Figure 6.12: Current signals measured at PV strings for DC arc-fault FP7

Figure 6.13: MFD outputs for PV string currents for DC arc-fault FP7

Series Arcing Faults: Four cases of series arc faults defined as, Fs1 to Fs4 were

simulated at the simulated low irradiance levels at the locations indicated on Figure

6.3. The general trend in the load current during all cases of series fault is graphically

illustrated by considering the case for series fault Fs2 at irradiance of 50 W/m2 shown

in Figure 6.15, and Figure 6.16 for load current and voltage respectively.

Series arcing fault introduces a restrictive arc resistance in series to the load

resulting in significant reduction in faulted PV string load current with almost no

backfed current from the unfaulted strings. This reduction is seen at the DC input of

the inverter. Figure 6.15(a) shows the drop in the load current at the inverter input.

The corresponding MFD output in Figure 6.15 (b) indicates the existence of

sustained DC arc-fault. The MPPT response by putting the PV system to operate at a

lower MPP as shown in Figure 6.16 (a) where the voltage is quickly restored to

500V. Even at the reduced MPP, the DC arc-fault is still sustained as indicated by the

respective current and voltage MFD outputs.


The series fault, Fs2 affects PV strings 1 and 2, hence the reduction in their

load currents shown in Figures 6.17 (a) and (b). The Persistence DC arc-fault is also

indicated on the respective MFD outputs of the PV string fault currents in Figure

6.18.

Figure 6.14: Current signal measured at DC bus and MFD out for DC arc-fault Fs2

Figure 6.15: Voltage signal measured at DC bus and MFD out for DC arc-fault Fs2

Figure 6.16: Current signals measured at PV strings for DC arc-fault Fs2


Figure 6.17: MFD outputs for PV string currents for DC arc-fault FP2

6.3.4 DOCAS Response to Changing MPP with Existing DC Arc-Fault

Condition

The MPPT algorithm usually responds to the changing environmental (varying

irradiance and temperature) conditions by optimizing the operating point of the PV

system to a new MPP to match the changing condition. This is acceptable in the

sense of regulating the DC bus voltage to maintain constant value. However, under

lower irradiance, the fast action of the MPPT can be problematic to the fault

detection system[118] .Under low irradiance, PV system already operates at lower

current, then if a fault occurs, the MPPT will respond to the fault by shifting the MPP

again to even lower point, thus reducing the backfed current making it even more

difficult for the fault detection mechanism to detect the existence of the fault. In the

case of the arc-fault, it will sustain even with lower current level.

To demonstrate the DOCAS algorithm’s operation under such conditions, a

case of parallel and series arc-faults were simulated on the DC bus at Fp7 and Fs3 for

different scenarios of simulated irradiance levels, and the fault current and voltage,

Idc, Vdc were respectively taken at the point of measurement. These tests were

performed to demonstrate further behaviour of the DC arc-fault detection with

persistence of arc-fault while the system undergoes transition in irradiance. It is also

intended to demonstrate the robustness of the algorithm to differentiate between fault

and non-fault conditions.

Transition from High to Low Irradiance with Existing DC Arc-Fault: DC

arc-fault exists at irradiance of 150W/m2 then transitions into lower irradiance of

100W/m2. This case demonstrates transition of the irradiance as the day progresses


towards the evening. The selection of the very low irradiance levels is intended to

demonstrate the effectiveness of the algorithm to detect DC arc-fault even at very

low irradiance when the irradiance changes while arc-fault exists. Figure 6.19 (a)

shows the DC arc-fault current with arc fault at 1.2s.

The transition from 150W/m2 to 100W/m2 occurs at 1.3s. Practically, the

transition is gradual, and will not result in sudden step change, however the intension

is to distinguish between a normal change and change under fault in the DC load

current. Figure.6.19 (b) shows the MFD output from the DOCAS algorithm where

the chaotic nature of the DC arc can be observed starting at 1.2s. The fast-changing

DC arc causes spikes to randomly appear in the MFD output. The action of the

MPPT restore the DC voltage at 820.5V (1pu) is demonstrated in Figure 6.20 (a)

with the corresponding MFD output in Figure 6.20 (b). This action results in the

system operating at a lower maximum power point while the arc fault is sustained.

Figure 6.18: DC arc-fault current and MFD output high to low transition after fault

Figure 6.19: DC arc-fault voltage and MFD output high to low transition after fault

Transition from High to Low Irradiance with DC Arc-Fault onslaught after

Transition: Figures 6.21 and 6.22 illustrate the scenario where the irradiance

transitions from 150W/m2 to 100W/m2 1.1s then arc fault occurs at 100W/m2 at 1.2s.

This scenario is to demonstrate the capability of the DOCAS algorithm to distinguish

between non- fault and fault condition.


In Figures 6.21(a) and 6.22 (a) the fault current and voltage are respectively

shown. Their corresponding MFD outputs are shown in Figure 6.21(b) and Figure

6.22(b) respectively. In Figure 6.21(b) a single tall spike above the prefault noise

spikes is visible at 1.1s. This spike is correlated to the change in irradiance.

Similarly, in Figure 6.22(b), the elongated spikes at 1.1s are related to the response

of the MPPT algorithm to restore voltage. Visibly, no sustained random MFD spikes

above the prefault noise spikes are present in both situations. Moreover, the prefault

noise level is reduced with reduced irradiance. The prefault noise level could be

illuminated by applying a noise threshold; however, that is not considered in these

simulations. In the same figures, the onslaught of the arc fault at 1.2s follows a rapid

growth in MFD spikes. These spikes are sustained, and the chaotic nature of the DC

arc is clearly visible. Thus, the algorithm generates sustained, taller MFD spikes in

response arc as compared to normal transition of irradiance.

Figure 6.20: DC arc-fault current and MFD output high to low transition before fault

Figure 6.21: DC arc-fault voltage and MFD output high to low transition before fault

Transition from Low to High irradiance with existing Arc Fault: Arc fault

exists at irradiance of 50W/m2 then transitions into higher irradiance of 75W/m2.

This case demonstrates transition of the irradiance as the day progresses from

morning upwards. The arc fault occurs at 1.2s, and the arc persists as the irradiance

transitions from 50W/m2 to 75W/m2 at 1.3s as illustrated in Figure 6.23 (a) and


Figure 6.24(a) in the DC current and voltage waveforms respectively. Figures

6.23(b) and 6.24(b) show the corresponding MFD outputs for the DC current and

voltage waveforms where the sustained random MFD spikes appear. Figure 6.24 (a)

shows the increase in DC voltage to 1.002 pu due to the increase in irradiance,

however this is regulated by the MPPT algorithm to 1 pu and maintain the MPP at

level above the level associated with 50W/m2.

Figure 6.22: DC arc-fault current and MFD output high to low transition with fault

Figure 6.23: DC arc-fault voltage and MFD output high to low transition with fault

6.4 CONCLUSION

The DC arc-fault detection strategy utilises the DOCAS algorithm. The

application of the DOCAS algorithm as a DC Arc-Fault Detector in this scenario

does not require any structural change to the algorithm in terms of mathematical

formulation of the MM filters and the SE used. The DOCAS algorithm has been

inherited with all designed parameters as applied in AC system fault detection.

However, the input signals to the DOCAS algorithm in its application as DC Arc-

Fault Detector are DC voltages and currents measured at various points on the DC

side of the PV system. Moreover, under this application the DOCAS algorithm

exhibits different characteristics as opposed to its application in AC fault detection.

DOCAS is a time domain algorithm, hence its easy and seamless application for DC

arc-fault detection. The application of DOCA in DC Arc-fault detection makes use of


the chaotic behaviour of the DC arc phenomena. When DC arc-fault occurs, the arc

is sustained, and the rate of change in displacement of the DC arc current and voltage

generate spikes in the MFD output. The MFD spikes are random will continuously

appear with existence of the sustained DC arc. The DOCAS algorithm is blind to

faults that do not generate arc, thus it is anticipated that it will operate in tandem with

the existing DC OC protection system in PV systems. The novelty in the application

of DOCAS as a DC Arc-Fault Detector is its ability to detect DC arc-faults under all

conditions including, low irradiance, partial shading, night to day transition.

Moreover, DOCAS is immune to the effect of the MPPT algorithm and can

continuously detect arc-fault under any MPP. Different cases of DC arc-fault at very

low irradiances, including 50 W/m2, 75 W/m2, 100 W/m2 and 150 W/m2 were

simulated to test the sensitivity and effectiveness of the DOCAS’ ability to detect DC

arc-fault. Moreover, the ability of DOCAS to differentiate between fault and non-

fault conditions were simulated with transition in MPP existing arcing fault

conditions under low irradiances. It was shown that, DOCAS could detect all fault

conditions as well as differentiate between faults and non-fault conditions.

Chapter 7: Conclusions and Future Directions 185

Chapter 7: Conclusions and Future

Directions

In this chapter, research outcomes from the thesis are summarised. The

significant research contributions are specified, and the benefits and importance of

the proposed methods are summarised. Finally, recommendations for future research

directions are suggested.

7.1 SUMMARY OF CONCLUSIONS

The main objective of this research work is to develop a fault detection and

diagnostic tool which can be applied in AC as well as in DC systems. MM which is a

nonlinear signal processing tool is used to develop the DOCAS algorithm. The MFD

output signal of the DOCAS algorithm is used to propose strategies for adaptive

radial distribution feeder OC protection, strategy for HIF detection and classification

based on feature extraction. The same algorithm is used for DC arc-fault detection in

PV systems.

The DOCAS MFD is a multistage filter algorithm that uses two nonlinear MM

called the MMF and two classes of ASFs filters called the open-close and close-open

ASFs. The performance of the algorithm was enhanced through the application of a

weighted eccentrically decreasing convex SE, g with five points and two unequal

slopes. The SE was decomposed into two smaller SEs, designated A1 and A2 each

with three points to preserve computational efficiency and applied at the decomposed

MMF filtering stages in cascade. Further two SEs designated B1 = A1 and B2 = g,

such that B2 > B1 where applied at the ASF stages.

The attributes of the DOCAS algorithm in AC system fault detection and

diagnosis has been evaluated through simulation and analysis of simple AC signal as

well as a SLG fault. The output of the algorithm provided characteristics and features

such as the fault detection windows and MFD edge spikes for application in adaptive

OC protection and HIF detection. Moreover, the attributes for HIF detection and

classification based on extraction of two HIF features; the randomness and arc

extinction and re-ignition were established. Furthermore, the MFD output provided

mechanism for the concept of memory update at a fixed time interval by the MFD

186 Chapter 7: Conclusions and Future Directions

edge spikes to adjust the OC threshold parameter and make it adaptive to changes in

distribution network topology and load changes. This technique is important and

provides the characteristic desirable for successful detection and identification of all

types of faults in feeders with high levels of RE based DER penetration.

One of the novelties in the DOCAS MFD algorithm is its seamless application

in fault detection and diagnosis in both AC and DC systems. The DC arc-fault on the

DC side of the PV system can be detected without any structural change. While there

is no structural change in the algorithm, its application in fault detection and

diagnosis in AC and DC system in PV systems exhibited different attributes. The

functional attributes of DOCAS algorithm in DC arc-fault detection in DC systems

were established through the simulation of a DC arc-fault. The concept of fault

detection windows and MFD edge spikes did not exist in DC arc-fault detection.

However, the DOCAS algorithm as a DC Arc-Fault Detector captures the chaotic

nature of the arc phenomenon by generating random chaotic spikes at its output

throughout as the DC arc is sustained when ignited.

The functional characteristics of the DOCAS algorithm in responding to the

test AC and DC arc-fault signal provided the mechanism for developing strategies in

adaptive OC protection in radial distribution feeders, features extraction for HIF

detection and DC arc-fault detection.

In developing strategy for adaptive OC protection in radial distribution feeders

with PV penetration, first a theoretical method based on Thevenin equivalent

modelling of the PV systems and feeder line and two distance factors based on PV

system location and distance to fault with respect to the feeder substation has been

developed. The impact of PV penetration on current contribution by the feeder

substation source established through the theoretical method. The observations made

through theoretical analysis are verified through simulations using the DOCAS

algorithm by simulating different faults conditions including, SLG, DLG and 3-phase

faults at various PV penetration levels and fault distances. The increased PV

penetration reduced the fault current contribution by the feeder substation source

which would have implication on the protection system coordination. The response

of the DOCAS is found to be consistent with observations made through theoretical

analysis. Using the simulated results and output of the DOCAS algorithm, a strategy

for setting an OC threshold which is adaptive to the changing network environment

and load condition with periodical update is developed. Moreover, through further


analysis of DOCAS output, it is observed that, while the increase in actual fault

current magnitude is lower with high level of DERs as compared to the fault current

magnitude due to the same fault condition without DERs, the percentage increase in

fault current magnitude from prefault condition to fault condition with DER is quite

significant. Based on this observation, a strategy for adaptive OC protection

incorporating the percentage increase in fault current magnitude and adaptive OC

threshold limit is developed. This strategy is adopted using the ITOC relaying

algorithm with appropriate scaling of pick up parameters at different DER (PV)

levels and fault distance. The adaptiveness and scalability of the strategy was

demonstrated by applying ITOC protection strategy. A minimum time delay of ¾

cycles + 1 sample (defined as Tdmax = 15.3125 ms) is required to declare an OC fault

before the ITOC relaying is initiated to determine the trip time. The proposed method

demonstrates that irrespective of the location of fault under any RE based DER

penetration level, the relay threshold (pickup) value is always maintained at the same

value. Furthermore, it was observed that protection coordination can be achieved at

different PV penetration level by appropriately selecting relay curves at different

TDS values.

A strategy for HIF detection using several classification techniques, including

Threshold and Timer classifications is developed using MFD outputs using fault

current and voltage signals. Specific regions of the MFD output related to the voltage

signal designated MFDv have been identified for the extraction of the HIF

randomness and arc extinction and re-ignition features. The DOCAS algorithm

generated spikes, called the MFDvTall edge, MFDvShort edge, MFDvWindow and MFDvArc

spikes at its output where the first three category of MFDv spikes are used in the

extraction of randomness feature while the fourth category is used in extraction of

arc extinction and re-ignition feature. The extracted MFDv spikes are subjected to

threshold classification to expose the randomness in time and height variation. The

extracted features are tested against the decision logic for the determination of HIF.

This strategy is tested against cases of HIFs on different contact surfaces simulated at

different test locations on the IEEE 13 bus test system. The DOCAS algorithm

successfully detected all simulated cases; however, the application of threshold

classification to segregate the MFDv spikes exceeding the fixed threshold limit above

15% of the prefault MFDv value of each category of spikes resulted in furthest fault

not detected. The HIF classification strategy is tested against cases of noise for


different SNR and it is found that DOCAS extracted the necessary features although

the noise reduced the rate of appearance of the spikes HIF is still detected. From

these observations, it is determined that, distance to fault and the SNR are factors

have to be considered in determining appropriate threshold limit. The undetected

case of HIF was detected by taking into consideration these factors.

The performance of DOCAS MFD algorithm as a DC Arc-fault Detector is

tested against cases of mismatch, parallel and series DC arc-faults on DC side of

simulated PV system. Moreover, tests are performed at very low irradiance of

between 50 W/m2 to 150 W/m2 to test the effectiveness of the method and its

performance under changing MPP and to differentiating a fault condition. The

DOCAS algorithm as DC Arc-Fault Detector showed remarkable performance in

detecting all cases of DC arc-faults as well differentiating faults conditions from non-

fault condition.

It has been demonstrated through simulations that; DOCAS MFD has the

potential for utilization in adaptive distribution feeder OC protection in distribution

feeders with PV penetration as well as HIF detection and classification in AC

systems. The utilization could be considered for backup protection in tandem with

existing protection scheme in systems where PV systems are being integrated.

Moreover, its ability to detect HIF could be used to complement the lack of capacity

in the existing feeder OC protection in detecting HIF faults.

In DC arc-fault detection, the DOCAS MFD algorithm could be implemented

together with the existing PV fault detection system as well as in any DC system to

provide an integrated protection scheme for reliable and secure protection against

faults including DC arc-faults.

7.2 FUTURE DIRECTIONS

The fault detection and diagnostic tool also referred to as the decomposed

open-close alternating sequence (DOCAS) algorithm has been developed using the

mathematical morphology signal processing technique. This algorithm has been

extensively tested for performance verification and characterisation involving three

different fault scenarios, including OC fault detection in radial distribution network

with increased penetration of DERs, high impedance faults detection and DC arc-

faults detection in PV systems


The simulation case studies provided valuable insights to the functional

attributes of the DOCAS algorithm, particularly its attributes for feature extraction

and fault characterisation in the detection and identification of various fault

conditions. While the algorithm has been successfully tested using synthetic data,

more work is required as part of furthering the investigation into the functional

characteristics of the DOCAS algorithm involving hardware simulation and using

data from physical systems. The suggested further/future work for algorithm

verification studies is summarised in Subsection 7.2.1.

The use of mathematical morphology as a signal processing technique to

developing tools for power system fault detection and condition monitoring is

gaining momentum. The lessons learnt during the research in developing the

DOCAS algorithm are provided in Subsection 7.2.2 to assist future research

involving MM signal processing technique.

The DOCAS algorithm has been extensively used in the simulation studies for

developing procedures for adaptive OC protection in AC distribution system. While

the algorithm has been used for DC arc-fault, it has not been used for DC power

system (non-arcing) OC protection. From the outset, the algorithm will require

structural changes for non-arcing fault detection in DC system as the fault detection

windows based on zero-crossing detection that made AC fault detection successful is

not available in DC fault signals. Thus, further research is required in proposing and

developing techniques for adaptive OC protection in both AC and DC power

distribution networks with increased DER penetration. A compilation of different

proposed techniques with their advantages and disadvantages are provided in

Subsections 7.2.3 and 7.2.4 to assist future research in AC and DC power system

protection.

7.2.1 Further Testing and Evaluation of the DOCAS Algorithm

The performance evaluation of the DOCAS algorithm has been analysed using

synthetic data generated from different fault conditions and scenarios. While every

effort has been made to ensure the simulated case studies closely resembled the

physical characteristics of the simulated conditions, further evaluation of the

algorithm would be necessary to fully realise the potential of the DOCAS algorithm

especially in responding to the actual fault condition. It must also be noted that, with

simulated case study, multiple fault conditions under varying environmental and load


conditions were simulated and the algorithm tested. This cannot be achieved through

staged test. Performing staged tests for fault conditions such HIFs will require

specialise equipment, and moreover, the power supply must be disconnected to

perform such test. Furthermore, data obtained from such staged tests are network

specific for the time period the tests were performed [152]. However, as fault data

from real systems become available, the DOCAS algorithm will be further evaluated.

Thus, in considering future direction to this research, further testing of the DOCAS

algorithm involving hardware simulations would be required. For such hardware

simulations, it would also be necessary for hardware implementation of the

algorithm. This will involve implementing a digital relay by programming the

DOCAS MFD algorithm into microchip then further testing involving tools such as

hardware in the loop to test the digital relay by simulating the fault conditions.

7.2.2 Research Direction for Developing Mathematical Morphological Fault

Detection and Diagnostic Tools

Mathematical morphology is a time-domain based technique that is used to

study random sets generated through various nonlinear transforms to extract

information about the sets. In signal analysis, the set contains quantitative description

of the geometrical structure and topography of the signal, and by means of MM

transforms this information can be extracted and displayed in complete time-domain

in either on the Euclidian space or Cartesian coordinate system.

In Chapter 3, the mathematical description of the fundamental MM transforms

that underpins the concept and mechanism in signal transformation have been

provided. The genesis of all MM transforms and techniques are the dilation and

erosion transforms. The dilation and erosions are dual transforms, however the action

of one cannot be undone by the other yet they operate so effectively when combined

to form different nonlinear MM filters. This has been effectively shown in the

development of the DOCAS algorithm.

In signal analysis and transformation using the MM techniques, the signal

profile is depicted directly in time domain as opposed to other techniques based on

integral transforms such as Fourier and Wavelet Transforms which process signal in

frequency domain. The MM signal processing technique involves the use of a

filtering probe called the structuring element (SE). The SE interacts with the signal

under investigation by sliding through it and capturing any variation in the signal to


extract the required features within the neighbourhood of the SE. The added

advantage in MM techniques is the mathematical simplicity which mainly involves

addition, subtraction, maximum and minimum operations as compared to the integral

transforms thus allowing for faster computation when investigating the same signal.

Moreover, MM exhibits superior functional characteristics when processing non-

periodical and distorted waveforms. Signals with deformities or complicated shape

are decomposed into certain parts and are separately kept from the background signal

for feature extraction while preserving the main characteristic of the signal. Thus,

this characteristic of MM allows it to extract information from the signals embedded

in noise while suppressing the noise and reconstruct the original signal [150].

The fundamentals and operational concepts of MM as portrayed in Chapter 3

and demonstrated in Chapters 4, 5 and 6 are formal and general enough that they can

be applied to the analysis of signals of any geometrical shapes for feature extraction

and fault detection. Thus, to realize fully the potential of MM technique in power

system signal analysis for fault detection and condition monitoring, research into

proposing and developing MM based tools should be encouraged. The following

provides a guideline to future research

❖ The MM techniques make use of strategic combination of the dilation, erosion,

open and close transforms to develop filtering functions. There is no specific

guide as to how to combine theses transforms. There are however, generalised

nonlinear filters available that can be used, and the reader should be curious

enough to explore the numerous reference sources cited in Chapter 3. Selection

of the generalise filter type and how to apply is not restricted, but the purpose

for the use of the filter should be the guide or basis on which the filter or

combination of should be determined.

❖ To minimize processing time, an appropriate data window size should be

selected. MM can use small size window for real-time signal processing. The

size of the data window can be determined based on the sampling rate.

❖ The structuring element is very fundamental to the effectiveness of the MM

based fault detection and diagnostic tool. Thus, the selection or design of an

optimal SE will enhance the functional efficiency of the MM filter for the

purpose it is designed for. The SE is a probing filter and collects information

when it interacts with the signal under investigation. Thus, MM is the study of


how different SEs interact with the signal under investigation [132]. It has been

noted that, difficulties in implementation can occur when large SEs are utilised.

Thus, decomposition of the large SE into smaller SEs for a combined

segmented implementation of the SE has been proposed as a solution by [158]

and applied in this research. The reader is referred to Section 3.4 and

Subsection 3.4.4 as a guide to designing and/or selecting the optimal SE

Furthermore, Gautam and Brahma [157] have made an attempt at providing

guidelines for selection of an optimal SE for MM based tools for detecting

power system disturbances, hence the reader is referred to this reference as a

starting point.

7.2.3 Research Direction towards the Development of Adaptive OC Protection

System for AC Distribution Networks Increased DERs

Section 2.2 provides a description of protection system devices for radial

distribution feeder which include devices such as OC relays, ACRs and fuses. These

devices are current sensing devices and are only triggered when a fault current above

the specified threshold rating such as the pickup setting of the relays, ACRs and the

fuse rating exist. These devices function in tandem and in a coordinated manner for

reliable, sensitive and selective OC protection.

The traditional method in distribution network protection system coordination

study requires considerable data including; network topology, distribution substation,

distribution feeders, loads and protective device characteristics [1]. The procedure

includes;

• Computing the available maximum and minimum fault MVA values at every

point in the distribution network feeder where a protective device will be

located. This is equivalent to computing the maximum and minimum values

of the Thevenin equivalent impedance at those locations. Usually these data

are obtained via load flow and short circuit (fault) studies.

• The threshold limit and rating of the protective devices are determined based

on the fault MVA information obtained.

The traditional method (procedure) in protection system coordination study still

holds ground, however additional and innovative means of maintaining protection

coordination in the presence of increased DER penetration is necessary, and


numerous researches have been conducted and are being conducted to proposed

reliable protection OC protection schemes in the presence of increased DER levels.

In Chapter 4 it was shown that the increased Penetration of DER at the

distribution network feeders reduces the fault current magnitude seen by the feeder

substation relay. This phenomenon was demonstrated analytically and by simulations

in Sections 4.2 and 4.7 respectively. The degree of fault current reduction is subject

to the level of penetration of the DER, the location of the fault with respect to the

location of the DER and the distance to fault from the feeder substation.

Furthermore, the available fault current is affected due to the intermittency of the RE

based DERs and network topology change. The reduction in fault current magnitude

will compromise the feeder OC protection with obvious being the designed (fixed)

threshold parameter setting (pickup setting and rating) of the OC relays, ACR and

the fuse respectively. If the generated fault current is less than 2 times the threshold

parameter setting of the protection device, the fault will not be detected by that

device. Moreover, the reduction in fault current magnitude leads to the following

protection system concerns;

Loss of Coordination of the Protective Devices: Protection system

coordination is maintained through correct operating time and sequence of operation

of each device within the protection scheme. The operating time is inversely

proportional to the detected fault current magnitude and based on the operating curve

of each device, the operating time with intentional delay is determined. The relay

operating curve is determined based on the time dial setting (TDS) and the plug

setting (PS) where both are fixed parameters, and the TDS is subject to the fault

current picked up by the relay. Loss of coordination results in sequential

misoperation or false operation of the relays depending on their location with respect

to the location of the fault and level of fault current magnitude with respect to their

point of placement [183],[184].

Reduced reach of the relay/Protection Blinding: Zone of protection discussed

in Section 2.2 defines the range of coverage (reach) of OC relays and depends on the

fault current pickup setting. The fault current pickup setting is a fixed parameter

determined based on the available fault current without DER penetration. Suppose a

fault occurs within the designed zone of protection of the substation relay, but due to

the presence of say a large scale DER closer to the fault location, the DER will have

an increased contribution to the fault current consequently reducing the fault current


available at the substation relay thus reducing the reach of the substation relay. And

if the increase in fault current magnitude is less than its fault current pickup setting,

the substation relay is blind to this fault [185].

False Tripping or Sympathetic: False or sympathetic tripping occurs when a

relay located near the substation on a feeder with DER also closer to the substation

trips when a fault occurs on an adjacent feeder coupled to the same substation.

Supposing R1 and R2 represent relays near the substation on the unfaulted and faulted

feeders respectively. Assuming the fault location on the faulted feeder is at a distance

away from the substation. It will take time for R2 to register sufficient fault current

above its pickup setting as compared to R1 which is fed by the DER closer to it.

Thus, R1 will trip before R2 due to the increased fault current from the DER [186],

[187].

Islanding Problems: Islanding problem occurs when a feeder (faulted or not)

is isolated from the grid with an active DER. The case of sympathetic tripping of R1

in the above scenario for the fault on the adjacent feeder is an islanding problem

where the DER and all loads on the unfaulted feeder are islanded [184],[187].

The issues highlighted are some of the concerns that must be taken into

consideration when designing or upgrading the OC protection schemes for

distribution network feeders with increased penetration of DERs. To overcome these

concerns, various methods and techniques have been proposed by different

researchers. The proposed techniques can be categorised as optimization based,

communication assisted based and other strategies which are summarised in Table

7.1 with their advantages and disadvantages. The list of methods presented is not

exhaustive, however is intended to show the direction for further research in adaptive

OC protection for AC distribution systems with increased DER penetration.

Table 7.1: Summary of OC Protection Schemes for Distribution Networks with Increased

DER Penetration

Method Main Feature Advantages Disadvantages

Online

modification of

OC relay

settings using

differential

evolution

algorithm

Centralise protection scheme.

The network status is

continuously monitored by

accessing data from multiple

relays and assumingly Digital

Fault Recorders (DFRs)

accessed via

communication/SCADA

▪ Almost real time

network status

update.

▪ Improves

protection

coordination

including

▪ System totally

depends on

maintaining active

communication and

SCADA

infrastructure.

▪ Failure of


[188]. system. Using the data, the

pickup setting on relays and all

associated protective devices are

updated using load flow, fault

and sensitivity calculations to

maintain coordination.

selectivity and

sensitivity

communication

infrastructure will

render the scheme

ineffective.

Minimise sum

of operation

time of OC

relays

coordination

using modified

firefly

algorithm

(MFA) [95].

The time setting multiplier and

the plug setting of the OC relays

are minimised using the MFA

optimisation algorithm

iteratively to arrive at a

minimised sum of OC relays

coordination time.

▪ Fast optimised

coordination with

DERs with

reduced time to

coordinate

▪ Method is effective

only in smaller

networks.

▪ More DERs and

large-scale network

will increase

convergence time

Optimization

of fault current

limiter (FCL)

connected at

the point of

common

coupling using

Cuckoo

optimization

algorithm

(COA) and

linear

programming

(LP) [189].

The method combines COA

and LP where COA is used to

determine the OC relay pick up

setting in the presence of the

FCL for both grid connection

and islanded mode and LP is

used to determine an optimum

time dial setting of the relay for

coordination.

▪ Fast convergence.

▪ One setting for

both mode of

operation

▪ The system was

tested without DERs

connected. Its

performance under

intermittent DER

switching and

network topology

change has not been

evaluated.

Communicatio

n assisted non-

linear

programming

minimisation

method to

reduce

directional OC

relays

operating time

[190].

The technique uses non-linear

programming method for

optimising the time dial setting

and the pickup setting of the

directional OC relays to

maintain coordination.

▪ No FCL required.

▪ No adaptive

feature necessary.

▪ Entirely dependent

on maintaining

active

communication

link.

▪ No backup

Optimization

of

superconductin

g FCL

resistance to

determine the

OC relay

settings [191].

The superconducting FCL

current from equivalent voltage

sources depends on its

resistance. Thus, OC relay

pickup setting for coordination

can be achieved by optimising

FCL resistance.

▪ Assume fixed

voltage source,

thus no voltage

optimisation

required to

achieve relay

coordination.

▪ Cost increases with

number of

superconducting

FCL with increased

penetration of

DERs.


Minimisation

of series FCL

resistance and

strategic

adaptive OC

relay

placement

[192].

The resistance of the FCL is

decreased below the critical

value and simultaneously

adjusting one relay setting for

adaptive relaying. The

placement of the adaptive relay

is critical to maintain

coordination with other relays.

▪ Reduced FCL

resistance will

reduce FCL size.

Reduce cost of

FCL

▪ Additional feature is

required for making

the selected relay

adaptive with

increased DER

penetration, load

condition and

intermittency.

Harmonic

distortion

restrained

protection

scheme [193] .

Non-linear optimisation

technique is used determine the

DER penetration level to

operate within acceptable

harmonic distortion limit as not

compromise protection scheme.

Protection system is designed

within this constrain as well

other protection requirements.

▪ Harmonic inject is

necessary for this

protection scheme

and suitable for

inverter interfaced

DERs.

▪ Scheme is only

limited to inverter

interfaced DERs

Optimised

DER sizing

and placement

to retain OC

relay settings

[194].

Genetic algorithm is used to

optimally size the DERs and

where to place them to avoid

compromising the relay

configuration to maintain

coordination.

▪ No changes to the

original relay

settings

▪ Restriction on the

growth of the power

system. Increased

penetration of

DERs will upset

coordination.

Adaptive

relaying using

state detection

algorithm

[195].

Individual OC relay setting is

updated through state detection

algorithm by detecting voltage

and current signals.

▪ Applicable in both

grid connected

and islanded

mode.

▪ Reliable under

steady state

operating condition.

▪ Coordination can be

lost during dynamic

load variations and

topological changes.

Critical relay

setting for

coordination is

estimated

based on

steady-state

network

reduction [196,

197].

The steady state relay settings to

maintain coordination are

calculated assuming the DERs

inject currents and the current

from the DERs estimated.

▪ The scheme

considers the

intermittency of

the DERs.

▪ The run time delay

increases with

network size.

Fault signal

magnitude is

tracked using

recursive DFT

with relay

setting

optimisation

achieved using

The power system signals are

continuously monitored f and

DFT is used to calculate their

magnitudes. These parameters

used by Fuzzy logic-based

optimization tool to set the

relays for coordination based on

network conditions.

▪ Limited to few

network

topologies

▪ System is not

scalable to cover all

possible network

topologies with

increased DERs.


Fuzzy logic

[45].

Adaptive

protection

based on

optimised

Thevenin

parameter

estimation

[44].

Communication network is

used to transmit data from relays

and DFRs to calculate the

Thevenin equivalent parameters

under fault and normal

conditions. Current is estimated

based on these calculations and

the relay settings are adjusted.

▪ Relay settings for

coordination is

achieved.

▪ Active

communication

network is required.

▪ Computational

delay will increase

with complexity of

the network.

7.2.4 Research Direction towards the Development of Adaptive OC Protection

System for DC Power Systems

Implementation of adequate protection schemes in DC distribution systems

are not well developed and still at their infancy as opposed to protection schemes in

AC distribution systems. While DC system OC protection schemes applies almost

identical principle as OC protection scheme in AC systems, direct adaptation of

protective devices such as CBs designed for AC systems is practically not possible.

The inherent lack of zero-crossing in the DC fault current will not actuate the trip

mechanism of the AC CBs. Moreover, if the AC CB does trip, the mechanical

contact separation will initiate arcing in the CB, and since there is no zero-crossing to

permit natural extinguishing of the arc, the arc will sustain, and this is undesirable.

Development of protective devices suitable for application in DC distribution

system constitute a major research area in proposing and implementing secure and

dependable protective schemes that would be reliable and sensitive while being

selective to ensure service continuity and enhance performance efficiency of the DC

system. Thus, future research directions in DC system protection include research

into developing DC protective devices and appropriate adaptive protective schemes

to satisfy performance requirements.

Therefore, this discussion is intended to create awareness in the current state

of research in proposing and developing different protection schemes for DC power

systems. The advantages and disadvantages of protective devices and techniques are

discussed to assist in directing research focus in the promising technologies.

In order to propose and/or develop suitable protection scheme for the DC

system, the nature of the faults and the fault signal (current and voltage)

characteristics must be understood. A typical DC distribution system is depicted in


Figure 7.1 consisting of PV array, battery energy storage system (BESS) and

connected to the utility AC grid via a bidirectional converter. The DC distribution

system derives energy from the PV array thought the unidirectional DC-DC

converter, the BESS which includes the bidirectional DC-DC converter and the AC

grid through the bidirectional AC-DC/DC-AC (rectifier/inverter) converter. The DC

system can aggregate multiple energy sources including RE based DERs, and

considering the intermittency of the RE based DERs, the energy sources are managed

by the energy management system (EMS) for intelligent load sharing. The DC loads

are connected to the DC system via DC distribution feeders. This system is not

immune to any fault conditions; largely short-circuit faults that can occur on either

the DC buses or the feeders.

Faults in DC Systems: DC systems can suffer from two possible cases of

short-circuit fault conditions. These are pole-to-pole short circuit which can occur

between the positive and negative conductors and pole-to-ground which can occur

between positive or negative conductor and the ground or both positive and negative

conductors shorted to the ground. The pole-to-pole fault condition generally results

in low fault impedance and can be more devastating with larger fault current

magnitude than the pole-to-ground fault with fault current path through the

grounding resistance. For a pole-to-ground fault current to exist, the system must be

grounded (which is not shown in Figure 7.1). Depending on the grounding resistance,

a pole-to-ground fault can have either low or high fault impedance which dictates the

magnitude of the fault current [198],[199].

Fault Locations: Short-circuit faults can occur at any location on the DC

system including DC buses and DC distribution feeders. Bus faults are more severe

as multiple energy sources can be connected to a bus and thus feed the fault

simultaneously as well as from sources on coupled adjacent buses. DC feeders are

connected to DC busses and during fault, the local sources connected to that bus

directly linking the feeder and closer to the point of fault supplies the larger

component of the fault current. The location of the fault thus dictates fault current

magnitudes depending on the number of energy sources feeding the fault and the

fault resistance at the point of fault with respect to the energy sources [200].

In Figure 7.1, the direction of current flow during normal operation is indicated

by the dash line arrow and the direction of the fault current during any of the short


circuit fault conditions is indicated by the solid line arrow. Thus, during fault, all

energy sources within the EMS through the power electronic converters inject fault

current. The amount of the fault current contribution depends on each DER and their

associated converters which also determine the fault resistance at the point of fault

with respect to the feeding sources. It is imperative to correctly determine the rating

of the fault current interrupting devices at the point of placement with respect to the

energy sources and their associated converters. Moreover, considering the

intermittency of the RE based DERs, the fault impedance will vary accordingly.

Thus, it is implied that the protection scheme must be adaptive to the changing

operating conditions to be effective.

DC System Short-circuit Fault Current Characteristics: During short-circuit

faults, the fault current rises to a certain maximum value then oscillates with an

exponential decay to a steady state value. The transition period in which the fault

current experiences the exponential decaying oscillation is the transient period and

the rate at which the oscillation decays to steady-state depends on the time constant

defined by the capacitance of the DC-link capacitor (C), the inductance (L) and

resistance (R) of the line seen at the point of fault. The transient period can be very

fast and maximum current experience during this period can be devastating for the

sensitive electronics. The power electronic converters have the inherent switching

Figure 7.1: A typical DC power distribution system


characters and reverse blocking diodes characteristic to protect themselves. However,

to ensure complete protection, protective devices must be able to respond within this

period to protect the system [201],[202].

DC System Protective Devices: Current sensing devices such as fuses and

CBs have been traditionally used as protective devices in DC systems. However,

considering the complex nature of DC fault current, simplistic devices such as fuses

and CBs on their own will not provide complete protection of DC systems.

Moreover, these devices inherently lack the functional flexibility for coordination

with other devices within the protection scheme for a reliable, sensitive and selective

protection of the DC system [203],[199].

In Table 7.2 a summary of the protective devices used in DC systems is

provided. The list highlights the trend in the development of DC protectives devices,

their main features, advantages and disadvantages. Implementation of any reliable

protection scheme is imperative on the effectiveness of the devices within the

scheme, thus future research in DC system protection also require research into

developing protective devices conducive for application in DC systems considering

the dynamics of the fault current signal as discussed.

Table 7.2: Summary of Protective Devices used DC Distribution Systems

Device Main Features Advantages Disadvantages

Fuses

[204],[205]

▪ Connected in series with

protected device for fault

current interruption.

▪ Operates when the thermal

(threshold) limit of the

fusible link made up of

copper or silver strip is

exceeded due to increased

fault current.

▪ Fuse rating (thermal limit)

of the fuse depends on

current-time and voltage.

▪ Cheap

▪ Can be used in hybrid

configuration for back

up protection.

▪ Can be used at the DC

feeders at the load end.

▪ Can interrupt high

inrush and surge

current from DC

equipment/device

switching.

▪ Selection of fuse rating

can be difficult due to

high fault current and

the small time constant

of the fault current

during the transient

period.

▪ Slower melting time of

the fusible link could

prevent the fuse from

interrupting fault

current with small time

constant.

▪ Conversely larger fault

current time constant

compared to the

melting time of the

fusible link can ignite

an arc.


▪ Does not have

flexibility for

coordination with other

protective devices.

DC Circuit

Breakers

(DCCB)

[206] [207]

▪ Connected in series with

protected device for fault

current interruption.

▪ Moulded-case CB

(MCCB) with thermal or

magnetic tripping

mechanism operates on

sensing fault current

exceeding the threshold

limit.

▪ Fast interruption of

fault current.

▪ Suitable for DC system

protection including

HVDC

▪ MCCB contact

mechanisms can fail to

separate due to the

small time constant of

the DC link capacitor

discharge current

▪ Could fail to clear fault

in sensitive LVDC

systems

▪ Could fail to clear faults

in highly inductive

systems.

Solid State

Circuit

Breakers

(SSCB)/Sw

itches

[208],

[209],

[210].

▪ Power electronics-based

CBs developed to

overcome the short

comings of Fuses and

DCCBs.

▪ The SSCBs utilise the

switching characteristics of

the solid-state power

electronic devices. Three

main SSCBs are:

- Gate-turn off thyristor

(GTO) switch,

- Insulated-gate bipolar

transistor (IGBT) switch,

- Insulated-gate

commutated thyristor

(IGCT) switch

▪ Applicable in LVDC

and HVCD systems

▪ Overcome the short

comings of fuses and

CBs

▪ Fast switching speed

▪ IGBT based switches

can withstand high

fault current

▪ IGCT switches

typically have low

losses

▪ GTO with emitter turn

off (ETO) allows for

high power application

and fast switching.

▪ IGBT switches incur

high power losses

▪ The ICGT is a

unidirectional switch

▪ The fast switching

characteristic of the

GTO/ETO switch can

be problematic at times

especially in LVDC

systems.

▪ One of disadvantage

power electronic

devices especially

systems with

MOSFETs is the high

voltage drop.

Digital

Relays

[211]

▪ Continuously monitors

network condition by

measuring voltage and

current signal as well as

calculating their

derivatives

▪ Work based on accurate

measurement of voltage

and current signal.

▪ Apart from the

calculating the di/dt and

dv/dt, multiple

algorithms using signal

processing techniques

can be installed.

▪ All measurements must

be coordinated and

synchronised.

▪ Active Communication

network is required for

IEDs such as relays to

maintain coordination

for intelligent

protection.


DC Distribution System Protection Schemes

There are two types of protection schemes widely researched and proposed for

implementation in DC systems, and these are known as the non-unit protection and

unit protection [212]. The discussions contained herein highlight some of the

advantages and disadvantages of these schemes as an attempt at directing further

research in proposing and developing sound protection schemes for DC systems.

Non-unit Protection Scheme: The Non-unit protection scheme requires

placement of fault current interrupting devices in series with the element to be

protected or at the point of protection. This scheme operates on similar principle as

the OC protection in AC systems where current is used as the threshold metric, such

that, if the fault current exceeds the pickup setting of the protective devices which

violates their normal limit of operation defined by their thermal or magnetic

characteristics, the fault current is interrupted.

The Non-unit protection depends on accurately monitoring and/or measuring the

voltage and current signals as well as the time derivatives of voltage (dv/dt), current

(di/dt) and the impedance at the point of fault [198],[213]. There is merit in this

technique; however, there is no flexibility in the technique being adaptive

considering the fixed threshold setting and/or rating of the protective devices, and the

intermittent nature of RE based DERs as well as multisource fault current. The

threshold parameters in the protective devices in the scheme must be adaptive to

ensure secure and reliable protection. Moreover, while being sensitive, the Non-unit

protection scheme lacks the flexibility for selectivity. The protective devices will

instantaneously operate as long as their threshold limits are violated, which is an

advantage as backup protection. Furthermore, the instantaneous tripping

characteristic presents difficulties in enabling coordination with other protective

devices within the non-unit protection scheme [201],[211].

To ensure secure and robust protection of the DC system, improvement to the

non-unit protection scheme would require research into making this scheme adaptive

while enabling the flexibility for selectivity and coordination [212].

Unit Protection Scheme: The unit protection scheme relies on the use of

intelligent electronic devices (IEDs) such as digital relays as current sensors to

continuously monitor the status of the network as well as maintaining an active

communication network to transfer network status information (fault data) between


relays on the adjacent nodes using algorithms such as generic object-oriented

substation events (GOOSE) [214]. The unit protection scheme has the flexibility to

provide reliable, sensitive and selective DC system protection [215]. However, it

lacks the flexibility to provide backup protection to zones or circuits outside the

designated zone of protection. Thus, must be implemented together with a non-unit

protection scheme to enhance the overall protection system reliability. Moreover, the

effectiveness of this scheme relies on maintaining an active communication network,

thus installing non-unit protection ensures backup protection in the event of

communication network failure [203].

There are two classifications of unit protection schemes, the Data-based

protection scheme and the event-based protection scheme.

Data-based Protection Scheme: In this proposed scheme, a single relay/unit

controls multiple CBs installed at specific nodes under the jurisdiction of that unit.

Fault data (current and voltage) are measured at the converter and/or the bus and the

feeders using digital fault recorders. This data is sent to the controlling unit and using

differential fault current technique or fault detection algorithm, the fault can be

detected. If fault exist, the controlling unit for the fault node or link sends trip signal

to the appropriate CB. A status update is communicated to all interconnected

adjacent units to issue trip signals to appropriate CBs on the other end of the faulted

link ensuring protection reliability in case of the adjacent CB failure, thus isolating

the faulted segment. This arrangement enables mechanism for zoning and selective

fault isolation. The data-based protection is data intensive and its integrity and

effectiveness depends on high speed data communication network and proper

synchronising between adjacent control units [214],[215].

Event-based Protection Scheme: In this proposed scheme, fault data is only

measured at the local bus that couples the local load feeder, hence it is less data

intensive as compared to data-based protection scheme. Each energy source

supplying its local load coupled to the local bus is equipped with a current

sensor/relay and associated CB. Fault is detection by the local relay (unit) is achieved

through techniques such as detecting the change in fault current time derivative

(di/dt) as applied in the non-unit protection schemes. Use of superconducting fault

current limiters (SFCL) has been proposed for use in this protection scheme where

the electrical distance can be appropriately optimised by optimising the impedance of

the SFCL for fault detection and localization. Other schemes requiring installation of


artificial inductive line impedances (AILI) at each feeder have been proposed in this

protection scheme. AILIs have the characteristic to significantly influence the di/dt

characteristic of the fault current [216].

The local protection units using the fault detection technique such as change in

di/dt determines if a fault has occurred. By sensing the rate of change of the fault

current each unit determines if the fault is a local fault, interconnected feeder fault or

an adjacent feeder or adjacent bus fault. This information is communicated to all

local bus protection units to determine the fault location and activate the appropriate

CB. In the event of a bus fault (severe case), the communication process is omitted

and the local protection unit closer to the fault bus takes immediate action [217].

Fault detection and isolation have said to have been achieved within 30 ms using this

technique.

Cost implication has always been cited as an impediment to the implementation of

unit-protection scheme due to additional cost of maintaining communication network

infrastructure and relays, sensors and DFRs, etc. that are necessary for the realisation

of this scheme. However, with increased interest in smart grid and DC power

systems there will be increase investment in sensors and communication

infrastructure thus enabling the deployment of such intelligent protection schemes

such as the unit-protection in both AC and DC distribution systems [218],[219].

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SMART PROTECTION SYSTEM FOR FUTURE …Smart Protection System for Future power System Distribution Networks with Increased Distributed Energy Resources i Keywords Adaptive Overcurrent