POWERAFRICA2012_adewole_ID25

7/31/2019 POWERAFRICA2012_adewole_ID25

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IEEE PES PowerAfrica 2012 Conference and Exhibition

Johannesburg, South Africa, 9-13 July 2012

Fault Detection and Classification in a Distribution Network Integrated with

Distributed Generators

A.C. Adewole and R. TzonevaCentre for Substation Automation and Energy Management Systems

Cape Peninsula University of Technology, Cape Town, South Africa

Phone: +27 021-959-6459, Email: [email protected]

Abstract This paper develops a methodology for application indistribution network fault detection and classification. The

proposed methodology is based on wavelet energy spectrumentropy decomposition of disturbance waveforms to extractcharacteristic features by using level-4 db4 wavelet coefficients.Thus, few input features are required for the implementation.

Different simulation scenarios encompassing various fault types atseveral locations with different load angles, fault resistances, faultinception angles, and load switching are applied to the IEEE 34

Node Test Feeder. In particular, the effects of system changeswere investigated by integrating various Distributed Generators

(DGs) into the distribution feeder. Extensive studies, verification,and analysis made from the application of this technique validatethe approach. Comparison with statistical methods based onstandard deviation and mean absolute deviation has shown thatthe method based on log energy entropy is very reliable, accurate,and robust.

Index Terms Discrete wavelet transform, distribution network,fault detection and classification, wavelet energy spectrum.

1.INTRODUCTION

The recent restructuring in electric power utilities over the

last decade has brought about the need for efficientgeneration and transfer (transmission and distribution) ofelectric power to load centers. The mode of power evacuation

is usually via overhead lines. Overhead lines are subject to

the forces of nature and other uncontrollable factors, thus

liable to faults.

An essential aspect of Abnormal Event Management

(AEM) is fault detection and diagnosis. In the past, most

research and development in power system faults detection

and diagnosis focused on transmission systems, and it is notuntil recently with the introduction of stringent fault indices

by regulatory bodies that research on power system faults hasbegun on the unique aspects of distribution networks. The

application of algorithms designed for transmission networkswhen used for distribution lines are prone to errors because of

the non-homogeneity, presence of laterals/tap-offs, radial

operation, and load taps along distribution lines. Therefore,

there is the need for contingency plans to troubleshoot faults

and expedite service restoration in order to reduce downtime.

Many diagnostic methods have been developed andproposed, but a perfect, dependable, and secure method is

still the objective of continuous research. Methods based on

Wavelet Transform (WT) for fault diagnosis were proposed

by [1]-[6]. Reference [7] proposed a method for fault

detection and classification in transmission systems using

wavelet and fuzzy logic. Similarly, [5], [8], [10] suggested

techniques using WT and Artificial Neural Network (ANN)

for transmission line fault detection and classification.

Another technique based on WT and Support Vector Machine(SVM) was proposed by [11] for power system disturbance

classifier in transmission systems. Reference [12] presented a

methodology for the classification of Power Quality (PQ)

disturbances using Wavelet Packet Transform (WPT) andfuzzy k-nearest neighbor classifier. A method by [13] for PQ

disturbances was based on Discrete Wavelet Transform

(DWT) and wavelet network. Reference [14] also presented a

WT and rule based method for power quality classification in

a transmission network. A method based on Hubbard-

Stratonovich (HS) transform and radial basis function neuralnetwork was suggested by [15]. Reference [16] described a

method for fault detection and classification based on WT

decomposition of the transformed current values. The method

suggested the use of wavelet entropies for multi-agent fault

diagnosis in distribution networks.

Although, the method by [15] is fast because of the

reduction in the computational requirements, the use of level-1 coefficients may fail to provide the appropriate transientcharacteristics that truly represent the fault type/phase(s)

especially where there is mutual coupling between the

phases. Also, the technique described by [14] did not cover

the effect of noise disturbance on the model. The method

proposed by [16] made use of Clarks Transform to convert

the three phase current measurements to modal domain. The

disadvantage of this is the added computation that would be

required during implementation. In addition, the effects ofload angle, load switching, and capacitor switching were not

considered in the various literature reviewed.In this paper, wavelet energy spectrum entropy based on

log energy is employed to detect and classify faults in atypical distribution network. This is implemented by taking

into account the distinct nature of distribution networks and

network changes that are likely to occur. To validate the

proposed approach, extensive simulation studies are carried

out on the IEEE 34 Node Test Feeder Benchmark model at

different fault locations, fault resistances, fault inceptionangles, load angle variations, load and capacitor switching,

and network topology changes.


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The rest of this paper is organized as follows: Section II

explains the principles of Wavelet Transform. Section III

describes the Power System Model. The implementation of

the fault detection and classification algorithm is outlined in

section IV. Section V provides the results and discussion ofthis approach. Section VI summarizes the conclusion.

II. WAVELET TRANSFORM ANALYSIS

A. Wavelet Transform

The classical Fourier Transform (FT) is a frequency

domain method. That is, it transforms a signal from time-

based to frequency-based one. Thus, time information is lost

and it is impossible to tell when an event took place. Short

Time (STFT) was introduced to correct the shortcoming of

the FT. However, a fixed time window is used. Many signalsrequire a more flexible approach where the window size can

be varied to determine the frequency or time more accurately.

A method such as WT capable of multiple resolutions in time

and frequency and with a flexible window size is thereby

required. The windowing in WT automatically uses short

time intervals for high frequency components and long time

intervals for low frequency components by using scale and

shift techniques.WT can be implemented using the Continuous Wavelet

Transform (CWT). The CWT of a signal )(tx is the integral of

the product between )(tx and the daughter-wavelets, which

are the time translated and scale expanded/compressed

versions of a function having finite energy, called mother-

wavelet.

The CWT of a signal )(tx is defined as [17], [18]:

dta

bttx

abaC

+

= )(

1),( (1)

where )(t is the mother wavelet, a is the scale factor, b is

the translation factor (position along the time axis), a2/1 is

the normalization value of )(,t

ba so that if )(t has a unit

length, then its scaled version )(,t

ba would also have a unit

length.

Another variant of WT is DWT. One area in which the

DWT has been particularly successful is transient analysis in

power systems [1], [2], [19]. This is because it acquires the

transient features and accurately analyzes them in both thetime and frequency contexts at different frequency bands with

different resolutions.

The mathematical expression for DWT is given by [17-20]:

=

km

m

m

knkfnmDWT

2

2)(

2

1),( (2)

where )(kf is a discrete signal,)(n is the mother wavelet

(window function), m and n are time scale parameters, k is

the number of coefficients, 2

m is the variable for scale, 2km

is the variable for shift, and 21m is the energy normalization

component to ensure the same scale as the mother wavelet.

In implementing Multi-Resolution Analysis (MRA) for

DWT, the scaling and wavelet functions are obtained from

[21], [22].

)22)( (

)2/(

,ntt

mm

nm

= (3)

)22)( (

)2/(

,ntt

mm

nm

= (4)

where )(, tnm is the scale function, and )(, tnm is the wavelet

function.

Wavelets are localized in both time (through translation)

and frequency (through dilation). The first scale covers a

broad frequency range at the high frequency end of the

spectrum and the higher scales cover the lower end of the

frequency spectrum. Signal decomposition starts by passing

the signal through a set of filters. Approximations are the

high-scale, low-frequency components of the signal producedby filtering with a low-pass filter with coefficient vector )(h

.The details are the low-scale, high-frequency components of

the signal produced by a high-pass filter with coefficient

vector )(g .

The filters are given by [22]:

)2(2)()( ntngt

n

= (5)

)2(2)()( ntnht

n= (6)

After each level of decomposition, the sampling frequency is

reduced by half. Then, the lowpass filter output

(approximation) is decomposed to produce the components ofthe next level. The original signal sequence )(kf can also be

represented by the sum of all components i.e the sum of all

the details and the approximation at the last level of

decomposition. For example, for two levels of

decomposition, the representation is:

)()()()()()(22111kcAkcDkcDkcAkcDkf ++=+=

)()()(1

kcAkcDkf l

l

jj

+==

(7)

where cDj

is the detail at scale j and cAl

is the approximation

at scalej, and l= 2.

B. Feature Extraction

Fault signals are known to contain transients and

harmonics. These high-frequency components carry essential

information that could be used to identify fault orabnormalities in power system network. The energy of

wavelet coefficient varies over different scales as per the


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energy distribution in the signal. Wavelet energy is the sum

of the square of WT coefficients.

The wavelet energy of a signal at scale jand instant k is

given as [23]-[25]:

)(

2

kDjEjk=

(8)

At scale j , the instants = 1, 2, 3, ..., N

The log energy entropy of the signal at scale j is:

=k

jkEEj EW log(9)

Standard Deviation is a statistical measure of distribution

or spread in a data set and it is derived from the square root of

the variance in a data set. Mean Absolute Deviation (MAD)is the mean of the absolute deviations of the data set from the

mean of the data. It shows the statistical dispersion of a dataset.

The standard deviation of the signal at scale j , instants k is:

( )= =N

k

jkj jDN 1

221

)(1

1

(10)

Similarly, the Mean Absolute Deviation of a signal is givenas:

=

=N

k

jjkDN

MAD1

1 (11)

whereDjk is the detail coefficient at scale j , instant k , j is

the mean at scale j , and N is the number of instants.

III. POWER SYSTEM MODEL

A. Base Case IEEE 34 Node Test Benchmark Feeder

The distribution network used is the IEEE 34 Node Test

Feeder. It is a long feeder operated at 60Hz with unbalanced

loading and nominal voltage of 24.9 kV. Fig. 1 shows the

IEEE 34 Node Test Feeder.

Fig. 1. IEEE 34 Node Test Feeder.

Simulation of the power system was carried out using

DIgSILENT PowerFactory and the steady state load flow

results were validated with the results from IEEE 34 nodebenchmark system in [26]. The relative error of the node

phase voltages is shown in Table 1.

Table 1. Node Voltage Relative Error vs. [26]

Dynamic electromagnetic transient simulation of differentfault types involving Single Phase-to-ground (1 Ph.-g), two

Phase (2 Ph.), two phase-to-ground (2 Ph.-g), and three phase

(3 Ph.) faults were performed. These simulations were carried

out at different locations at an interval of 10% along the main

feeder, at 95% of the main feeder, and on the laterals. Faultresistances )(Rf of 0, 2.5, 5, 10, 20, and 100, and

fault inception angles (fa) of 0o, 30o, 45o, 60o, and 90o were

used in the simulations. The fault inception angle fa is thephase angle of phase A voltage at the fault inception time.

Simulations were done to discriminate between transients due

to switching conditions from load and capacitor switching.Load angle variations of 0o, 60o, 90o were also carried out.

The waveforms were generated with a sampling rate of 128

samples per cycle.

B. Modified IEEE 34 Node Test Benchmark Feeder

Three cases which involved the integration of Distributed

Generators (DGs) into the benchmark model were studied inthis paper. These include:

DG1 case study: maximum load + 20% of DG

installed at node 840

DG2 case study: maximum load + 20% of DG

installed at node 844

DG3 case study: maximum load + 10% of DGs

installed at node 840 and 844 respectively.

Distributed generation refers to the electric power

generation (usually between 5kW and 10MW) at theconsumption end of a distribution network. The generated

power is integrated to the distribution network at the

substation, feeder, or customer load levels [27]. DGs can be

implemented with wind turbine, hydro, PV, fuel cells, etc.

The integration of DGs into a distribution network often

causes protection coordination issues [28].

Case studies involving the integration of DGs into the IEEE

34 node test feeder were carried out to investigate the abilityof WT based log energy entropy to correctly detect and

classify faults even after DGs were integrated.

Relative Error

(%)

Phase A Phase B PHASE C

Minimum -5.2032 -1.5586 -0.2325

Maximum

Average

-0.0307

3.1509

1.2722

0.7719

2.3210

0.8746


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The studies carried out in this paper did not assign any

specific energy source to the DG. Also, the parameters of the

synchronous generators were based on previous work carried

out by [28]. The connection of the generator to the grid was

via a 500kVA step-up transformer. The transformerimpedances were also set equal to the transformer at node

832 (XFM-1).However, the transformer winding was changed to delta-

star type based on the recommendations by [27] on optimal

transformer winding types for DGs. The placement and sizing

of the DGs were based on [28]-[31]. Thus, node 840 (along

the main line) and node 844 (one of the laterals) were used

with a 20% penetration level one at a time. Furthermore,another test case was simulated with smaller DGs co-located

in the network at nodes 840 and 844 respectively. A plot of

the voltage profile is given in Fig. 2. Similarly, a plot of the

short circuit currents at various nodes are shown in Fig. 3.

Nodes 836-1 refers to lateral 836-862, while Node 836-2

refers to lateral 836-840. The voltage profile plot shows the

impact of the integration of DG into the feeder. Also, there

was an increase in the short circuit current at various nodes inthe feeder.

Fig. 3. Voltage Profile of the various Case Studies

IV. ALGORITHM FOR FAULT DETECTION AND

CLASSIFICATION

A. Feature Extraction

Various simulations were carried out in DIgSILENT

PowerFactory. The waveform plots of the three phase andzero sequence currents were exported to MATLAB as ASCII

files. These files are decomposed into coefficients using db4

level-6.

Daubechies 4 (db4) is one of the most used wavelet in

power system disturbance analysis and it was chosen for this

research because of its orthogonality, compact support in the

time domain, and for its good performance in power system

studies as reported by [11], [12], [32], [33].

The lowpass filter )(g and highpass filter )(h of the db4

have four coefficients. These coefficients are:4830.0,8365.0,2241.0,1294.0

4321==== gggg

1294.0,2241.0,8365.0,4830.04321

==== hhhh

The particular level of decomposition to use is based on the

wavelet spectra. The log energy entropy, standard deviation,and mean absolute deviation at levels-1 to -6 were computed

using (9) - (11). Level-4 was chosen as the level of interest

for both fault detection and classification because the best

results for log energy entropy, standard deviation, and mean

absolute deviation were obtained at that level. Level-4

corresponds to the frequency range of 240Hz to 480Hz.

Fig. 4. Short Circuit Current for the various Case Studies

B. Design of Rule Based Detector and Classifier

The proposed algorithm in this paper is implemented with

software subroutines written in MATLAB. The fault

detection module is activated first and on detection of a faultcondition, the fault type and faulted phase(s) module is

triggered to perform the classification tasks. Each fault has its

characteristic feature or signature by which its faulted

phase(s) can be identified.

The fault detection module compares the computed level-4

entropy values with a predetermined threshold )(d for each

of the phases. The predetermined threshold )(d is carefully

800 810 820 830 840 850 860 870 880 8900.95

0.96

0.97

0.98

0.99

1

1.01Voltage Profile

Node

P

h.

Vab(p

.u)

DG Case 1

DG Case 2

DG Case 3

Base Case

1 2 3 4 5 6 7 8 9 100

100

200

300

400

500

600

700

800

900

1000

N-800

N-8

08

N-8

16

N-8

24

N-8

54

N-832

N-8

58

N-8

34

N-8

36-1

N-8

36-2

N = Node

Short Circuit Current for Case Studies

Nodes

MaxShortCircuitCurrent/(A)

Base Case

DG Case 1

DG Case 2

DG Case 3


5/8

chosen to ensure that the algorithm would be able to

accurately discriminate between faults and normal switching

events. In this particular case, da = 100, db = 100, and

dc= 100. Fault is detected when any of the computed

wavelet entropy values )(),(),( cWEEbWEEaWEE for the

three phases is greater than dp . ),,( CBAp . whereda ,

db , anddcare the entropy value thresholds for phase A, B,

and C respectively.

When fault is detected, the fault classification module is

triggered for fault type classification and faulted phase(s)

identification. The patterns observed through exhaustive

simulations were used to draw up the rules for the algorithm.

Fig. 5. is a flow chart for the implementation of this

algorithm.

The criteria for fault classification are:

R1: if )(aWEE > )(ca , & )(bWEE < )(cb & )(cWEE < )(cc

A-g FaultR2: if )(aWEE < )(ca , & )(bWEE > )(cb & )(cWEE < )(cc

B-g Fault

R3: if )(aWEE < )(ca , & )(bWEE < )(cb & )(cWEE > )(cc

C-g Fault

R4: if )(aWEE > )(ca , & )(bWEE > )(cb & )(cWEE < )(cc

AB Fault

R5: if )(aWEE < )(ca , & )(bWEE > )(cb & )(cWEE > )(cc

BC Fault

R6: if )(aWEE > )(ca , & )(bWEE < )(cb & )(cWEE > )(cc

CA Fault

R7: if )(aWEE > )(ca , & ( )(bWEE ) > )(cb & )(cWEE < )(cc

AB-g FaultR8: if )(aWEE < )(ca , & ( )(bWEE ) > )(cb & )(cWEE > )(cc

BC-g Fault

R9: if )(aWEE > )(ca , & )(bWEE < )(cb & )(cWEE > )(cc

CA-g Fault

R10: if )(aWEE > )(ca , & )(bWEE > )(cb & )(cWEE > )(cc

3Ph. Fault.

where )(),(),( cWEEbWEEaWEE are the computed level-4

log energy entropy values for phases A, B, and C. )(ca ,

)(cb , and)(cc are the fault classification thresholds for

phases A, B, C respectively and was set to 100.

2Ph. and 2Ph.-g faults were classified using the values of0IWEE . Line-to-ground faults exhibited higher zero sequence

entropy ( 0IWEE ), thus, this formed the basis for 2Ph. and

2Ph.-g classification.

Therefore, faults with values of 0IWEE > -250 will be

classified as 2 Ph.-g faults.

V. RESULTS AND DISCUSSIONS

A. Results

The proposed method was tested using several fault cases

comprising of various fault types, fault conditions, and

system parameters. In particular, the line segments at thebeginning and at the extreme end of the feeder were studied.

Initialization

Select the nextevent

3 Ph. Fault

Select level -4 detail coefficients

DWT level -6 decompositionusing db4 mother wavelet

PrintResult

No

Yes

Select 3Ph. & zerosequence currents

waveforms

dpWEEp >

WEE IWEE p 0, CBAp ,,Compute log energy entropy

Fault?

Single PhaseFault?

2 Ph. Phase Fault ?

2 Ph.-g Phase

Fault?

A-g, B-g, C-g

AB, BC, CA

AB-g, BC-g, CA-g

Yes

Fig. 5. Flowchart of the Proposed Algorithm


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Fig. 6. Distribution Plot of 2Ph. Fault for DG1 Case Study

The effects of the following were considered: Faultresistance, fault distance, fault inception angle, and the

integration of DGs. Fig. 6. is a visualization of the

distribution or spread of 2 Ph. faults for 10% to 95% of the

main feeder, laterals 820-822, and 846-848 respectively for

DG1 case study using log energy entropy. This shows that the

fault types are quite distinguishable from one another. From

the results obtained through several simulation cases, the

faulted phase was seen to have the highest log energy

entropy. The text in bold signify the faulted phase(s).

Statistical methods have been reported to show good

performance in power system analysis [34-39]. The proposed

method based on wavelet log energy entropy is comparedwith that based on features from Standard Deviation and

Mean Absolute Deviation (MAD) of the WT decomposition.Tables 2-4 show some of the results obtained for log energy

entropy, standard deviation and mean absolute deviation

respectively.

Table 2. DG1 Case Study at 10% of the Main Feeder (Rf= 0 , f = 0o)

Method

No Fault

(0o) Load

Angle

No Fault

(60o) Load

Angle

No Fault

(90o) Load

Angle

Load

Switching

Capacitor

844

Switching

1 Ph.

A-g

2Ph.

A-B

2Ph.

A-B-g

3Ph.

)(aWEE 62.16 26.74 64.64 59.2 77.73 219.22 188.20 213.95 217.055

)(bWEE 9.76 53.38 12.03 8.42 32.71 36.65 126.39 159.20 165.31

)(cWEE

)0(IWEE

)(a)(b)(c)0(I

)(aMAD

)(bMAD

)(cMAD

)0(IMAD

47.94

-451.03

3.68

3.00

2.75

0.20

2.12

1.82

1.80

0.12

10.59

-414.94

3.42

2.87

3.12

0.23

2.08

1.87

1.77

0.14

42.45

-414.94

3.16

3.11

3.03

1.83

1.94

1.90

1.83

0.24

45.45

-474.87

3.67

2.99

2.75

0.20

2.12

1.82

2.57

0.13

38.67

-440.38

4.34

3.45

2.81

0.21

2.53

2.11

1.88

0.13

83.38

-136.23

25.86

6.54

5.87

6.83

13.55

3.52

3.17

3.62

38.16

-506.73

33.23

32.69

3.27

0.13

13.30

12.95

2.25

0.091

73.36

-132.63

27.21

51.15

11.57

20.69

13.54

19.51

5.44

8.91

229.449

-820.41

26.33

34.50

42.89

17.84

13.48

14.29

18.33

4.28

Table 3. B-C Fault at Line 846-848 (Rf= 2.5 , f=30o)

Case

Study)(aWEE )(bWEE )(cWEE )0(IWEE )(a )(b )(c )0(I )(aMAD )(bMAD )(cMAD )0(IMAD

Base

Case

19.94 154.22 170.03 -279.02 1.79 6.11 6.56 0.45 1.54 3.95 4.10 0.25

DG1 46.34 138.03 146.82 -282.16 3.52 4.85 5.16 0.13 2.17 4.88 5.48 1.93

DG2

DG3

61.89

20.01

137.41

165.35

122.88

127.53

-255.06

-282.67

3.52

3.84

4.63

4.66

4.72

4.56

0.11

0.09

2.51

2.73

3.105

3.09

2.99

2.87

0.07

0.06

Table 4. C-A-G Fault at Line 820-822 (Rf= 5 , f = 60o)

Case

Study )(aWEE )(bWEE )(cWEE )0(IWEE )(a )(b )(c )0(I )(aMAD )(bMAD )(cMAD )0(IMAD

Base

Case

188.90 21.41 194.09 -69.81 13.04 3.27 13.74 4.85 6.61 2.14 6.64 2.01

DG1 185.5 30.55 180.87 -82.18 9.76 4.11 8.86 4.24 5.46 3.00 5.04 1.78

DG2

DG3

136.9

144.4

22.69

12.99

159.07

154.47

-142.2

-165.48

9.56

8.91

4.01

2.73

8.93

9.35

3.92

0.18

5.22

4.63

2.39

1.88

4.92

5.04

1.40

0.09

B. Discussion


7/8

The algorithm was able to differentiate between fault events

and no fault conditions like load switching, capacitor

switching, and steady-state system operation when log energy

entropy, standard deviation, and mean absolute deviation

were used as inputs. Tables 2-4 show the results obtained atfault locations close to the upstream substation, a lateral, and

at a location 189,205 ft. away from the upstream substationrespectively. Table 2 present some of the values obtained for

fault detection at 10% of the main feeder (Line 806-808).

Tables 3 and 4 show the results for faults at various fault

inception angles and fault resistances. For all the methods

presented, the faulted phase is associated with values many

times greater than the healthy/unfaulted phase(s). For faultdetection and classification using log energy entropy, the

various fault types were quite distinguishable for all the case

studies. Furthermore, the thresholds used for the fault

detection and classification for the base case performed well

even for the DG cases without the need to review these

thresholds. Simulation plots and entropy results showed the

existence of mutual coupling in the phases especially for

faults in close proximity to the DG location. However, the

algorithm was able to accurately distinguish between the

healthy phase(s) and the faulted phase(s). Although, different

entropy values were obtained for different combination of

fault resistances and fault inception angles for the same

location, that did not affect the detection and classificationperformance since the results obtained per fault types were

apparently above the pre-defined thresholds.The values obtained for and MAD after the integration

of DGs were not completely useful for fault classification

because there were similarities between the values obtained

for faulted phase(s) in one location and the values for healthy

phase(s) at another location. Table 5 illustrates some of the

errors obtained when using and MAD. Entry 1 of Table 5was misclassified as no fault. Entry 2 was also misclassified

as A-B fault. Entries 3 and 4 were wrongly denoted as no

fault respectively. Also, the values of and MAD showed a

corresponding decrease in value for faults with high

resistances and for faults located far away from the

substation. This implies that and MAD are influenced by

fault resistance and fault location.

Table 5. Features Using Standard Deviation and Mean Absolute Deviation

CaseStudy

Location Fault

Type

Fault Parameters )(a )(b )(c )0(I )(aMAD )(bMAD )(cMAD )0(IMAD

DG1 Line 834-842 A-g Rf= 0, f= 0o 5.12 3.85 3.55 1.99 3.43 2.57 2.30 1.16

DG1 Line 834-842 B-g Rf= 0, f= 0o 9.25 10.25 4.05 4.39 5.31 4.53 2.30 1.97

Base Case Line 828-830 A-g Rf= 100, f= 0o 5.43 2.98 2.88 2.08 3.46 1.93 2.00 0.86

Base Case Line 834-842 A-g Rf= 100, f= 0o 5.35 3.39 3.35 2.67 3.34 2.17 2.21 1.02

VI. CONCLUSION

This paper proposes an accurate approach for fault

detection and classification of fault types and faulted phase(s)

in distribution networks. Various scenarios were simulatedusing DIgSILENT PowerFactory. DWT was implemented in

MATLAB to decompose the three phase and zero sequence

current waveforms using db4 level-4 detail coefficients. A

rule based method was used afterwards for fault detection and

classification tasks respectively. It was observed that the

proposed method based on wavelet log energy entropy

accurately detects and classify the fault type. Comparisons

with statistical feature extraction methods based on

computation of standard deviation and mean absolutedeviation of the WT decomposition show that the method

based on log energy entropy is very reliable, accurate, robust,

and is independent of system conditions/changes. That is, it

provided accurate results irrespective of load angle variation,

load and capacitor switching, and network topology changes.

It is also immune to varying fault location, fault resistance,

and fault inception angles. It has been shown to be suitable

for conventional distribution network as well as modified

network with DGs. As a result of its simplicity, accuracy andspeed of operation, it can be used to aid existing protection

equipment in fault diagnosis.

ACKNOWLEDGEMENT

This research work is funded by the South African

National Research Foundation (NRF) UID62364 Substation

Automation and Energy Management Systems.The authors

are grateful for the financial support.

REFERENCES

1 Z.G. Bo, A.T. Johns, and R.K. Aggarwal, R.K., A Novel Fault Locator basedon the Detection of Fault Generated High Frequency Transients.Developments in Power System Protection, IEE Conference Publication, No.

434. pp. 197-200. 25-27th Mar. 1997.

2 Z.Q. Bo, G. Weller, and M.A. Redfern, Accurate fault location technique fordistribution system using fault-generated high-frequency transient voltagesignals.IEE Proceedings of Generation, Transmission, and Distribution, Vol.

146, No. 1, pp. 73-79, Jan. 1999.

3 A.M. Gaouda, M.M.A. Salaina M.K. Sultan, and A.Y. Cliildiaoi, PowerQuality Detection and Classification using Wavelet-Multiresolution Signal

Decomposition. IEEE Transactions on Power Delivery, Vol. 14, No. 4, pp.1469-1476, Oct.1999.


8/8

4 A. Abur, and F.H. Magnago, Use of time delays between modalcomponents in wavelet based fault location Electrical Power and

Energy Systems,No. 22, pp. 397403, 2000.

5 K. Gayathri, N. Kumarappan, and C. Devi, An Apt method for FaultIdentification and Classification on EHV Lines using Discrete Wavelet

Transform. Power Engineering Conference IPEC, Singapore, pp. 217-

222, 3-6 Dec., 2007.

6 O.A.S. Youssef, Combined Fuzzy-Logic Wavelet-Based FaultClassification Technique for Power System Relaying.IEEE Transactions

on Power Delivery, Vol. 19, No. 2, pp. 582-589. 2004.

7 R.N. Mahanty, and P.B. Dutta Gupta, A fuzzy logic based faultclassification approach using current samples only. Electric Power

Systems Research, No. 77, pp. 501507, 2007.

8 K.M. Silva, B. A. Souza, and N.S.D. Brito, Fault Detection andClassification in Transmission Lines based on Wavelet Transform andANN.IEEE Transactions on Power Delivery, Vol. 21, No. 4, pp. 2058-

2063, 2006.

9 V.S. Kale, S.R. Bhide, P.P. Bedekar and G.V.K. Mohan, Detection andClassification of Faults on Parallel Transmission Lines using WaveletTransform and Neural Network. World Academy of Science, Engineering

and Technology, No. 46, pp. 927-931, 2008.

10 M. Geethanjali, M. and K. Sathiya Priya, Detection and Classification inTransmission Lines. International Conference on Control, Automation,

Communication and Energy Conservation, pp. 1-7, 4-6 June, 2009.11 S. Ekici, S. Yildirim, M. Poyraz, Energy and entropy-based feature

extraction for locating fault on transmission lines by using neural network

and wavelet packet decomposition, Expert Systems with Applications,No.34, pp. 29372944, 2008.

12 B.K. Panigrahi and V.R. Pandi, Optimal feature selection forclassification of power quality disturbances using wavelet packet-based

fuzzy k-nearest neighbor algorithm IET Generation, Transmission,Distribution, Vol. 3, Iss. 3, pp. 296306, 2009.

13 M.A.S. Masoum, S. Jamali, N. Ghaffarzadeh, Detection andclassification of power quality disturbances using discrete wavelet

transform and wavelet networks. IET Science, Measurement,

Technolology. Vol. 4, Iss. 4, pp. 193205, 2010.

14 H.K. Chuah, P. Nallagownden, and K.S. Rama Rao, Power QualityProblem Classification Based on Wavelet Transform and a Rule-Based

method. IEEE International Conference on Power and Energy (PECon2010), Kuala Lumpur, Malaysia, pp. 1-6, Nov 29 - Dec 1, 2010.

15 S.R. Samantaray, P.K. Dash, G. Panda, Fault classification and locationusing HS Transform and radial basis function neural network, Electric

Power Systems Research, No. 76, pp. 897905, 2006.

16 El-Zonkoly A.M. Fault Diagnosis in Distribution Networks withDistributed Generation, Smart Grid and Renewable Energy, pp. 1-11,

February 2011.

17 M. Vetterli and C. Herley. Wavelets and Filter Banks: Theory andDesign.IEEE Transactions on Signal Processing. Vol. 40, No. 9, pp.

2207-2232, Sep. 1992.

18 A. Borghetti, S. Corsi, C.A. Nucci, M. Paolone, L. Peretto, and R.Tinarelli, On the use of continuous-wavelet transform for fault location

in distribution power systems. Electrical Power and Energy Systems,

No. 28, pp. 608617, 2006.

19 A.S. Yilmaz, A. Subasi, M. Bayrak, V. M. Karsli, E. Ercelebi,

Application of lifting based wavelet transforms to characterize powerquality events. Energy Conversion and Management, No. 48, pp. 112

123, 2007.

20 S. Ekici, S. Yildirim,M. Poyraz, A transmission line fault locator basedon Elman recurrent networks. Applied Soft Computing, pp.341347,

2009.

21 S. Burrus, R.A. Gopinath, and H. Guo, Introduction to Wavelet andWavelet Transform: A Primer.Prentice-Hall, 1998.

22 J.C. Goswami, and A.K. Chan, Fundamental of Wavelets: Theory,Algorithms and Application.John Wiley & Sons, 1999.

23 H. Zhengyou, F. Ling, L. Sheng, and B. Zhiqian, Fault Detection andClassification in EHV Transmission Line Based on Wavelet Singular

Entropy. IEEE Transactions on Power Delivery, Vol. 25, No. 4, pp.2156-2163, 2010.

24 H. Zhengyou, H.E. Shibin, G. Xiaoqin, C., Jun, Z., Zhiqian, and Q.Qingquan, Study of a new method for power system transients

classification based on wavelet entropy and neural network. Electrical

Power and Energy Systems, Vol. 33, pp. 402410, 2011.

25 A.M. El-Zonkoly, Fault diagnosis in distribution networks withdistributed generation. Electric Power Systems Research Vol. 81, pp.

14821490, 2011.

26 IEEE Distribution System Analysis Subcommittee. Radial Test Feeders[Online]. Available:

http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders.html .

27 P. Barker, R.W. de Mello, Determining the impact of distributedgeneration on power systems: part 1 Radial power systems. In Proc.

IEEE Power Eng. Soc. Summer Meeting, Vol. 1, pp. 16451658, 2000.

28 J.A. Silva, H.B. Funmilayo, and K.L. Butler-Purry, Impact of DistributedGeneration on the IEEE Node Radial Test Feeder with OvercurrentProtection.Proceedings of 39thNorth American Power Symposium. pp.

49-57, 2007.

29 R.C. Dugan and W.H. Kersting, Induction machine test case for the 34-bus test feeder-description. IEEE Power Engineering Society General

Meeting, pp. 1-4, 2006.30 S. Santoso, and Z. Zhou, Induction machine test case for the 34-bus test

feeder: a wind turbine time domain model. IEEE Power Engineering

Society General Meeting, pp. 1-4, 2006.

31 N. Samaan, T. McDermott, B. Zavadil, and J. Li, Induction machine testcase for the 34-bus test feeder-steady state and dynamic solutions.IEEE

Power Engineering Society General Meeting, pp. 1-5, 2006.

32 N. Perera, and A.D. Rajapakse, Power system transient classification forprotection relaying. 13th International Conference on Harmonics andQuality of Power, ICHQP, pp. 1 6, 2008.

33 F. B. Costa, B. A. Souza, and N. S. D. Brito, Real-time detection of fault-induced transients in transmission lines,IET Electronics Letters, pp. 753-

755, May 2010.

34 P.K. Dash, M.V. Chilukuri, B.K. Panigrahi, Power Quality Analysis and

Classification using a Generalized Phase Corrected Wavelet Transform.Power Electronnics, Machines and Drives, Conf Pub No. 487, pp.610-

615, 16-18 April, 2002.

35 P. K. Dash, B. K. Panigrahi, and G. Panda, Power Quality AnalysisUsing STransform.IEEE Transactions on Power Delivery, vol. 18, No.

2pp. 406-411, April 2003.

36 W. Kanitpanyacharoean, S. Premrudeepreechacharn. Power QualityProblem Classification using Wavelet Transformation and Artificial

neural Network IEEE PES Power Systems Conference and Exposition,

vol. 3, pp. 1496-1501. 2004.

37 G.G. Pozzebon, G.G. Pena, A.F.Q Goncalves, R.Q. Machado, NeuralClassification of Power Quality Disturbances: An Application of the

Wavelet Transform and Principal Component Analysis 9 th IEEE/IAS

International Conference on Industry Applications (INDUSCON), pp. 1-6.

2010.

38 C. Venkatesh, D.V.S.S Siva Sarma, M. Sydulu, Classification of voltagesag, swell and harmonics using S-transform based modular neuralnetwork 14thInternational Conference on Harmonics and Quality of

Power (ICHQP),pp. 1-7, 2010.

39 J. Christy X. Jeno Vedamani S. Karthikeyan , Wavelet Based Detectionof Power Quality Disturbance - A Case Study. Proceedings of 2011

International Conference on Signal Processing, Communication,

Computing and Networking Technologies (ICSCCN 2011) pp. 157-162,2011.
http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders.htmlhttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=Authors:.QT.Perera,%20N..QT.&newsearch=partialPrefhttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=Authors:.QT.Rajapakse,%20A.D..QT.&newsearch=partialPrefhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4662486http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4662486http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders.htmlhttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=Authors:.QT.Perera,%20N..QT.&newsearch=partialPrefhttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=Authors:.QT.Rajapakse,%20A.D..QT.&newsearch=partialPrefhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4662486

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