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Sung In Cho
On-Line PD (Partial Discharge)
Monitoring of Power System Components
School of Electrical Engineering
Thesis submitted for examination for the degree of Master of
Science in Technology.
Espoo 09.09. 2011
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Abstract
AALTO UNIVERSITY ABSTRACT OF THE
SCHOOL OF ELECTRICAL ENGINEERING MASTER‘S THESIS
Author: Sung In Cho
Title: On-Line PD (Partial Discharge) Monitoring of Power system Components
Date: 09.09.2011 Language: English Number of pages: 13+135
Department of Electrical Engineering
Professorship: Power systems and High voltage Engineering Code: S-18
Supervisor: Prof. Matti Lehtonen
Instructor: D.Sc. (Tech.) Petri Hyvönen
Condition based maintenance has emerged as a priority issue in modern power
systems, and has reminded so for last several decades. Appropriate monitoring
and diagnosis before severe faults occur make it possible to control and operate
power systems in a more reliable, effective, and sustainable way. Compared other
monitoring techniques, Partial Discharge (PD) monitoring seems the most
promising methodology for detecting possible dielectric breakdown, aging and
ultimately faults in power system components. In order to maximize the benefits
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Acknowledgements
This thesis was done in the department of Electrical Engineering in Aalto University
School of Electrical Engineering in Espoo, Finland in collaboration with Doble
Lemke in Dresden, Germany. To begin with, I truly appreciate to my supervisor, Prof.
Matti Lehtonen, with his guide and supports during this thesis work. In addition, I
also would like to express my gratitude to D.Sc. (Tech.) Petri Hyvönen, instructor, for
his guide, advice and encourage. Thanks to Dr. Stefan Kornhuber, engineering
manager in Doble Lemke, I can finalize my thesis work in a more fruitful, valuable,
and reliable way with his precise comments and critical advice. Moreover it is very
important to express my appreciation to the Service team in Doble Lemke and other
kind staffs especially for the one who took me to the city centre when I lost my last
bus at the first day in the Kesselsdorf.
I certainly appreciate my friends so-called ―Otaniemi Family” in Finland who has the
same family name, ByungJin, KyungHyun, and EunAh. I am deeply thankful to all
b h i d KOSAFI b h ll h i d I l
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List of Abbreviations
PD Partial Discharge
IEC International Electrotechnical Commission
CBM Condition Based Maintenance
HF High Frequency
VHF Very High Frequency
UHF Ultra High Frequency
AE Acoustic Emission
UPS Uninterruptible Power Supply
HVE High Voltage Equipment
GIS Gas Insulated System
TEAM Th l El i l A bi d M h i l
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AM/FM Amplitude Modulation/ Frequency Modulation
k-NN K Nearest Neighbour
NN Neural Network
BNN Back propagation Neural Network
PNN Probabilistic Neural Network
PSA Pulse Sequence Analysis
DP Degree of Polymerization
FDS Frequency Domain Spectrum
PDC Polarization/Depolarization Current analysis
FRA Frequency Response Analysis
C&PF Capacitance and Power Factor
C&DF Capacitance and Dissipation Factor
IRA Impulse Response Analysis
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XLPE Cross-linked Polyethylene
PVC Poly Vinyl Chloride
EPR Ethylene Propylene Rubber
TDR Time Domain Reflectometry
FTRC Frequency Turned Resonant Circuit
ITRC Inductively Turned Resonant Circuit
HVDC High Voltage Direct Current
PDIV Partial Discharge Inception Voltage
GPS Global Positioning System
TCP/IP Transmission Control Protocol/Internet Protocol
RTU Ring Main Unit
PILC Paper Insulated Lead Cable
MIND Mass-Impregnated Non-Draining paper insulated cable
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TE Transverse Electric Wave
TM Transverse Magnetic Wave
UI User Interface
PC Personal Computer
RF Radio Frequency
TF map Time/Frequency map
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List of symbol
aC Capacitance of the test object
bC Stray capacitance of the PD source
cC Internal capacitance of PD source
k C Measuring Capacitor
1U Applied test voltage
2U Voltage drop across the PD source
3U Voltage drop across the m R
mC Measuring capacitor
m R Measuring resistor
sG Grounding switch
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0 R Reading of the PD instrument
0q Known calibrating charge
i P The probability of appearance for that value i x in the i-th phase
u The mean value
2 The variance
1
1
i
i
dy
dx The differential coefficient before and after the peak of the distribution
i x The average discharge magnitude of the positive half cycle
i y The average discharge magnitude of the negative half cycle
sQ The sum value of discharges of the mean pulse height distribution in
the negative cycle
sQ The sum value of discharges of the mean pulse height distribution in
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initial DP Initial DP
e Euler ‘s number
1C The HV capacitance of the bushing
2C The LV capacitance of the bushing
L The test inductance (or external inductor)
C The cable capacitance
c The cut-off wave length
a The outer radius of the conductor
b The inner radius of the conductor
0c The propagation velocity of the signal (30cm/ns)
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Table of Contents
Abstract........................................................................................................................... i
Acknowledgements ....................................................................................................... ii
List of Abbreviations ................................................................................................... iii
List of symbol ............................................................................................................... vii
Table of Contents .......................................................................................................... x
CHAPTER 1 ................................................................................................................. 1
1 Introduction .......................................................................................................... 1
1.1
Motivation................................................................................................. 1
1.2
Condition Based Maintenance on Power System ..................................... 3
1.3
PD monitoring in power system ............................................................... 5
1.4
Thesis Overview ....................................................................................... 6
1.5
The aim of the Thesis ............................................................................... 7
CHAPTER 2 ................................................................................................................. 8
2 PD measurement System ..................................................................................... 8
2.1
PD monitoring system configuration ........................................................ 8
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3.3.2 Time resolved method ............................................................................ 37
3.3.3 3-Phase Amplitude Relation Diagram (3 PARD) ................................... 38
3.4 PD feature extraction and de-noising ..................................................... 38
3.4.1 Noises in PD ........................................................................................... 39
3.4.2 Gating and Windowing ........................................................................... 39
3.4.3 Pulse arrival time difference ................................................................... 40
3.4.4 Digital filter method ............................................................................... 41
3.4.5 Signal processing method ....................................................................... 41
3.4.6 Statistical method.................................................................................... 42
3.4.7
PD pulse shape method ........................................................................... 44
3.5
PD pattern classification ......................................................................... 44
3.5.1
Distance classifier ................................................................................... 44
3.5.2 Neural Network (NN) ............................................................................. 45 3.5.3
Support Vector Machine (SVM) ............................................................ 46
3.5.4
Pulse Sequence Analysis (PSA) ............................................................. 47
3.6
Signal processing of PD signal ............................................................... 48
CHAPTER 4 ............................................................................................................... 49
4 PD M it i P S t C t 49
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4.3.3 Different diagnosis and monitoring techniques on rotating machines ... 75
4.3.4 On-line PD monitoring on rotating machines ......................................... 77
4.3.5 Available products for on-line PD monitoring of RM ........................... 79
4.3.6 Summary and Conclusion ....................................................................... 80
4.4 GIS (Gas Insulated System).................................................................... 81
4.4.1 GIS in power system ............................................................................... 81
4.4.2 PD types in GIS ...................................................................................... 83
4.4.3 Different diagnosis and monitoring techniques on GIS ......................... 84
4.4.4 On-line PD monitoring on GIS ............................................................... 85
4.4.5
Available products on-line PD monitoring of GIS ................................. 87
4.4.6
Summary and Conclusion ....................................................................... 89
4.5
On-line PD monitoring on power system components ........................... 90
CHAPTER 5 ............................................................................................................... 91
5
Conclusion and Future work ............................................................................ 91
References ................................................................................................................... 93
Appendix 1: CASE STUDY 1 ....................................................................................... 116
Appendix 2: CASE STUDY 2 ....................................................................................... 120
A di 3 CASE STUDY 3 125
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CHAPTER 1
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monitoring power systems which are the most intricate system humans have ever
made in history.
Compared to many protection methods in power system, Partial Discharge (PD) is
considered as one of the most promising solutions for monitoring and detecting
possible faults in the system before they occur. Thanks to the development of other
engineering areas such as radio communication, computer science and signal
processing, protection systems are becoming cheaper and more robust, also highsensitivity. PD is able to find possible symptoms of faults in the system in the most
fundamental and simplest way.
With IEC 60270 and other standards regarding PD monitoring, PD measurement
techniques and calibration had been established with detailed explanations for
monitoring purposes. Since direct detection of PD is not possible, conventionally
technicians have been using so-called ―apparent change‖ detection. Whilst traditional
methods are detected after failure or discrete periodic interval monitoring, modem
techniques are largely dependent on the relative changes of important parameters in
time or frequency domain. As a result, Condition Based Maintenance (CBM) has been
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1.2 Condition Based Maintenance on Power System
The most significant issue for industrial utilities is the protection of possible faults
which usually incur tremendous repair cost and inconvenience to the customer. Even
though Uninterruptible Power Supply (UPS) makes it possible to operate electrical
equipment in hospital or factories that require a stable and continuous power supply,
unexpected power interruption increases the possibility of large scale disaster and
cascade blackout. Therefore utilities have been developing proper monitoring system
for power system in order to predict and prevent electrical faults before they occur.
Largely, there are two considerable reasons for CBM.
1. Maintenance of good operating condition has become a priority for preventing
penalty cost and protecting expensive electric High Voltage Equipment (HVE).
2. With technological progress in computer science, signal processing, and radio
communication, CBM operating with reasonable price and reliable accuracy has
arisen [2].
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Figure. 1.1 TEAM stress on Power System Components [7, 8]
Regarding the monitoring insulation system of power system components, there are
four main influencing factors affecting the lifetime of the insulation system, known as
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2. Appropriate signal processing makes it possible for on-line PD monitoring
without noises around the monitored equipment.
3. Sensors and tools for on-line PD monitoring are widely available at a relatively
reasonable price.
4. Continuous PD information with analysis facilitates possible life prediction
modelling of HVE [5].
5. On-line PD monitoring is possible while the system components are in operation
otherwise they need to be disconnected and tested in the laboratory, entailing
expensive costs for conducting off-line tests [6].
Since measuring electromagnetic field change is effective even in a noisyenvironmental and while power components are in operation, on-line PD monitoring
on power system components will provide enough information for CBM, operating
the power system in a safe, reliable and, predictable way.
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placement internal or external of HVE. During sensing, back ground noise signal from
different system components can be mixed with PD signals from the examined
component. Therefore, noise deduction obtained from the sensor ‘s signal generates
important PD features in order for a more precise diagnosis. These features have its
distinct characteristics so that it is possible to classify them by comparing with prior
data from the laboratory or on-site. This process is known as ―pattern recognition‖ or
―pattern classification‖. By doing so, the PD monitoring system finally estimates the
possible fault type. Finally, all of this process can be used for life prediction
modelling of the HVE. Based on all of the information from PD sensing to life
prediction, a more precise PD monitoring system is possible. Moreover on-line PD
monitoring based on the above diagram makes it possible for real-time monitoring
data analysis, resulting in a robust CBM operation.
1.4 Thesis Overview
The thesis consists of 5 chapters. The first chapter will explain motivations and a
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1.5 The aim of the Thesis
The objective of the thesis is be a holistic review of existent PD measurement,interpretation algorithms and applications for on-line monitoring of high voltage
power system components from a theoretical and practical perspective. The aim is to
not only collect the data related to PD monitoring system, but also categorization and
discuss of each application and PD monitoring system is presented. In addition, the
thesis clarifies the following questions regarding PD monitoring systems:
1. What kinds of methods are currently used to monitor a PD signal in power system
components?
2. What kinds of sensors are currently used on different power system components to
detect PD and its location?
3. How can the PD signal extracted from a noisy environment for on-line PD
monitoring?
4. Based on different sensors, how can fault situation be defined according to the PD
signal pattern?
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CHAPTER 2
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Along with the growth of condition based monitoring system on power systems,
effects have been made to apply PD detection systems in-real time while power
system components are in operation. In this sense, it was pointed out that biggest
problem of the conventional method with IEC 60270 is the high ratio of noise level
per PD signal. Therefore recently different PD detection schemes such as ultra high
frequency method, acoustic, optical and chemical detection have been developed to
overcome the high level of noise without intricate signal processing. Moreover a new
standard of PD detection using electromagnetic and acoustical methods have arisen
named IEC 62478 in the near future. For this reason, this chapter will describe general
system configuration from conventional PD detection system for apparent charge
measurement and unconventional PD monitoring systems.
2.1.1 Conventional PD monitoring system (IEC 60270)
Conventional PD monitoring refers to PD measurement method according to the IEC
60270 standard [10], measuring induced apparent charge in the detection circuit.
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impossible owing to inaccessibility to the PD spot inside of the test object. The simple
equivalent capacitor arrangement of system layout so-called a-b-c model and
measuring system is shown in Figure2.1.
Figure. 2.1 Simple capacitive a-b-c model and measuring mechanism [9]
aC = Capacitance of the test object which is not affected by any PD
bC = Stray capacitance of the PD source
cC = Internal capacitance of PD source
As we can see, three capacitance values represent capacitance of the insulation
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Apparent charge [9, 10]
Figure. 2.2 Apparent charge measurement equivalent circuit [9]
1U = Applied test voltage
2U =Voltage drop across the PD source
3U =Voltage drop across the m R
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3 1( )
a
a m
C U U
C C (2.1)
Simplification of the equation with the consideration thatmC is much higher than aC
3 1m a aU C U C q (2.2)
Taking into account thatbC ≪ aC , the equation can also be expressed as
1 2a a bq U C U C (2.3)
By multiply aC / aC , the final equation will be
2 a b ba c
a a
U C C C q q
C C (2.4)
In other words, above equation describes that the discharge occurred at cC will causes
a voltage drop as 1U which will be transmitted through bC to the capacitance aC by the
ratio as bC / aC .Therefore the measureable charge ( aq ) is a certain portion of actual
charge ( cq ) at the PD site due to the fact that bC / aC ≪1. We should note that the
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According to the IEC 60270, the relationship of frequency spectrum of PD and
measuring frequency band was covered. Firstly, the integrated part of PD should be
assumed as constant within measured frequency band width. Secondly, the upper and
lower frequency band cut-off ( 1 f and 2 f ) should be lower than measured constant
part of PD value. Lastly the recommended gain gap between frequency spectrum of
PD and measuring frequency band should be less than 6dB. Recommended frequency
band widths in IEC 60270 standard can be categorized wide and narrow bandmeasurement shown below.
Wide band measurement
Lower limit frequency: 30 kHz< 1 f
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Figure. 2.4 Basic coupling mode in series with the coupling capacitor [11]
Figure. 2.5 Basic coupling mode in series with the test object capacitor [11]
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Figure. 2.6 Polarity discrimination coupling mode [11]
Figure. 2.7 Balanced coupling mode [10]
Additionally, this coupling requires interrupting the grounding connection of the test
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Figure. 2.8 Coupling device described by IEC 60270 [9]
k
C = Coupling Capacitance
aC = Test object virtual capacitance
m R = Measuring resistor
m L = Shunt inductor
mC = Measuring capacitor
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of the PD instrument0
( ) R . The relationship of the series capacitance of the calibrator
( 0C ), test object ( aC ), and coupling capacitor ( k C ) can be expressed according to theIEC 60270 as shown in below.
0 0.1 ( )a k C C C (2.5)
Commercially available calibrators inject a known pulse (0 0 0q C U ) with certain
time intervals connected near the coupling device shown in Figure 2.10. This can alsoensure the connection of the whole measurement system. The following equation can
simply explain how to calculate the calibration factor.
0
0
a
k q q
R (2.6)
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power system components. The third version of IEC 60270 presents detailed
information from a coupling device to the calibration method as seen above. Even
though this method is vulnerable to noise and other interferences, the biggest
advantage over unconventional PD measurement system is the availability of the
estimated magnitude of PD.
A recent paper [16] pointed out some fundamental limitations of the conventional
method with three points; integration error in case of non-linear, possiblesuperposition error, calibration limits, and unknown attenuation of PD signal from PD
spots to sensors. Those challenges tackle the advantages of the conventional method
in terms of accuracy of the measurement system. Nevertheless, IEC 60270 has been
widely used as an application for new power system components testing and
commissioning, on-site measurement, and laboratory tests for periodic examination.For on-line application, calibration procedure and high signal to noise ratio makes it
difficult to apply the IEC 60270 method. However transformer application such as
multi-terminal measurements and GIS application for sensitivity verification have
sometimes been combined with the unconventional method which will be covered in
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Unconventional PD detection methods [20]
Electrical detection [21]: Electromagnetic measurement of PD consists of couplingdevices and data acquisition unit. The most suitable frequency band for application
regarding each power system components are shown in Table 2.1.
Cable Transformer GISRotating
Machine
HF (3 - 30MHz) O - - OVHF (30 – 300MHz) Δ O O O
UHF (300M – 3GHz) Δ O O -
Table 2.1 Suitable frequency band according to system components (O=Good, Δ=OK, -=NO)
Appropriate sensors and its placement on test object detect electromagnetic signal.
Detection of electromagnetic transient signal from PD occurrence is usually
performed by capacitive or inductive sensors. More detailed information regarding
system configuration, sensor type, and placement according to each system
components is covered in chapter 4. The main advantage of this method is its
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Optical detection [25, 26]: Optical emission from PD can be detected by optical
sensors. Unlike electrical signals from PD, optical signals largely depend on different
factors such as insulation material, temperature, PD intensity and pressure. The
spectrum of hydrogen or nitrogen depending on the surrounding material is the most
dominant concerning the spectrum of PD. There are roughly two kind of optical PD
detection techniques as a result of different kind of ionization, excitation and
recombination processes during the discharge; direct detection of optical PD signal
and detect of change of an optical beam. Detection of optical signal includes surface
detection and the detection inside of the test object such as GIS and transformer. For
cable application, corona emits the spectrum range around 280nm to 410nm at high
voltage transmission line which can be detected by a UV-visible camera during the
daytime. The rationale behind this is the ultra violet radiation ranging from 240nm to
280nm tends to be absorbed by the ozone layer. The optical sensors transferring signal
to the outside at photomultiplier, also can be placed inside the test object which is
efficient for a light-tight GIS impulse voltage test. This impulse voltage test is not
suitable for an electrical PD detection system. Another method called opto-acoustic
measurement catches sonic or ultrasonic range acoustic emission caused by PD which
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Electrical Acoustical Optical Chemical
Advantage
• Applicative forall kinds ofHVE
• Intensity,source, type,location of PDis assessable
• The mostsuitable forcontinuous on-line PDmonitoring
• High sensitivity•
Immunityagainstelectrical noise
• Very efficientfor localizationof PD
• Relatively lowcost
• Immunityagainstelectrical noise
• High sensitivity• Location of PD
is assessable(insome case)
• Test is possiblefor impulsevoltagecondition
• Immunityagainstelectrical noise
• Easy tomeasure
• Provide criticalinformation forGo/No Godecision
Disadvantage
• Highelectromagneticinterference
• Relativeexpensive cost
• Low signalintensity
• Not good forcontinuous PDmeasurement
• No informationaboutmagnitude ofPD
• No informationabout location,source,intensity, andtype of PD
Possible
Sensors
CapacitiveInductive
Piezo-electrictransducersCondensermicrophones
Optical fibreUV detector
photomultipliertube
DGA SensorsSF6 Sensors
Main
applicative All HVETransformer Cable, GIS
TransformerGIS
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Unconventional PD measurement system
Unconventional PD measurement is much more suitable for on-site and on-line PDmeasurement in which the external interferences largely influence the measured
signal. Especially electromagnetic wave and acoustic detection has been widely used
in the field since these two methods simply provide sufficient information concerning
the existence of PD and its possible location covering almost all kinds of power
systems components. As seen below in the Table 2.3, possible on-line application ofdifferent system components can be realized by nonconventional PD measurement
systems.
Cable Transformer GISRotating
Machine
Acoustic Δ O O O
Electromagnetic O O O O
Optical - - - -
Chemical - O - -
Table 2.3 Possible on-line PD detection techniques on power system components [37]
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object, test engineer and so on. In particular the nonconventional methods have not
been supported by standard, resulting many different test set ups regarding higher
frequency and other energy detection from PD occurrence. Standardization will
bolster the analysis of the correlation of the measured quantities from both methods.
On the other hand efforts have been made to combine the two techniques in order to
overcome each drawback. In particular a combined solution is effectively applicative
on transformer and GIS. This kind of integrated approach can detect PD occurrencewith accuracy and scalable quantity in a low noise environment. In this section,
correlation of the two measuring systems and its combining approach will be covered.
Conventional versus nonconventional PD monitoring
Fundamentally, PD measurement systems according to IEC 60270 and
nonconventional methods are measuring different quantities, apparent charge and
electromagnetic waves or others, even if it comes from the same source. Some
questions have arisen regarding the correlation between the two different methods and
interpretation of results [21]. The general comparison is shown below in Table2. 4.
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Measuring quantity Apparent charge
Transient earth voltage or current pulse ( Electromagnetic wave)Acoustic, Chemical by products,Optical spectrum
Measuring systemCoupling device, transmissionsystem, measuring instrument
Sensing components, transmission path, data acquisition unit
Noise Level Relatively high Relatively low
Application type
Mostly Off-line (Laboratory, On-site)On-line (Transformer)
Off-line and on-lineOn-line (Electrical, Chemical)
Table 2.4 Comparison of conventional and nonconventional method
*typical narrow band width for HF/VHF is 2MHz
**Typical wide band range is 50MHz or higher
***Zero span mode for individual frequencies or for specific frequency range
between 4 and 6MHz or higher
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Figure. 2.11 Example of combining PD measurement methods on Transformer [29]
2.3 On-line VS Off-line PD measurement system
In this thesis, on-line PD monitoring means the system with following requirements
• PD measurement while the test object is in normal operation
• Continuous PD monitoring (Trendable)
• Permanent installation of PD coupling device
•
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sometimes cannot be detected using the off-line method because it is carried out in
different circumstance to that of real cases such as load condition, vibration,
temperature, humidity and so on. That means the test object which passes for off-line
PD test can have potential failure in the power grid. This method, moreover, is
expensive due to outage during PD measurement.
However off-line PD measurement usually has high sensitivity and accuracy because
of relatively low back ground noise and is very suitable for new equipment qualitycontrol. For on-line PD measurement, on the contrary, the measurement is very
realistic because it performed under the real circumstances. The cost is relatively less
expensive and it is possible to have trendable data for the test object, meaning that the
life cycle management can be possible with on-line PD monitoring. The main
research ongoing in the on-line PD monitoring field concerns signal processing due tohigh noise combined with a true PD signal. However recent papers and commercially
available on-line PD measurement systems ensures effective on-line PD measurement
with appropriate signal processing techniques.
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UHF/AE technique can determine the concrete status of the test object. However
different measuring configurations of UHF/AE make it difficult to have strict linearity
and correlation. In this sense, draft IEC 62478 can assist to clarify the promising
UHF/AE detection configuration and make it more robust in the near future.
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CHAPTER 3
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3.1 Detectable PD signals
Partial discharge is detectable in a different way due to the fact that it generatescertain reactions according to the insulation materials in the system components.
Generated signals from PD are usually detectable in an electric, acoustic, chemical,
and optical way [44]. Electrical and chemical signals are referred to for finding out
PD occurrence in high voltage equipment, and acoustic signals are used to localize the
spot where PD takes place. Depending on the characteristic of the power systemcomponents, appropriate signal detecting can differ. Nowadays, combining of the
methods guarantees more accurate PD detection. In [45], a more physical approach to
the PD mechanism is presented.
3.1.1 Electrical signal
PD occurrence in the power system equipment makes the electrical signal. That is
because partial discharge brings about electron transfer in a short current impulse
within nanoseconds [1]. As described in the Chapter 2, in order to detect electrical
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3.1.2 Acoustic signal
Even though electrical signals are the obvious evidence of PD occurrence, acoustic
signal generated from the mechanical wave of a small explosion around the spot
where the PD takes place is widely used for PD monitoring [46]. The biggest benefit
of acoustic signal is the immunity from electromagnetic interferences [47, 18].
Moreover acoustic detection is not an intrusive method compared to other
measurement types [46]. In addition, the acoustic signal detection method is favoured
for localizing PD in the test object. By using several acoustic sensors on the object
which have PD occurrences inside, the computation of travelling time difference from
each sensor provide geometric information of PD location [48]. However even though
acoustic signals represent is against electrical interferences, acoustic noise or
mechanical vibration from other high voltage equipment can affect acoustic signal
strength.
3.1.3 Chemical signal
Partial discharge also creates a chemical reaction with the insulation material. One
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Coupled Device (CCD) Cameras can detect optical signals with relatively higher
sensitivity in air tight test objects such as GIS.
3.2 Sensors
In this section, the sensors used regarding PD detection are covered. Currently there
are many sensors which have been used depending upon the measuring method andtest object. Since the sensor plays an essential role in PD measuring configuration,
appropriate selection and its location can affect the measurement result significantly.
The basic requirements of PD sensors are below [52]
• Be able to sense and record measuring quantities from PD source for a set of
defined frequency bands
• Can differentiate between PD signal and background noises
• Small enough in order to be attached to the test object
The sensors traditionally detect PD below 500kHz due to technical limits and lack of
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version of HFCT is commercially available as shown in Figure 3.1. The HFCT detect
PD up to several hundred MHz.
Figure. 3.1 Commercially available core closed and split type of HFCTs
Rogowski coil [56, 57]: The Rogowski coil is a proper sensor for PD working on the
inductive principle with frequency bandwidth between 1 to 4 MHz. The Rogowski
coil has a structure of a circular plastic mold with a winding mounted for a uniformly
distributed density of turn with frequency dependant characteristic. By mounting
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capacitors has to be designed in order to withstand 60 Hz high-voltages, and it should
be manufactured to have low inductance in order to have good high-frequency
response. These two considerations are the reason for the relatively high price
compared to for example radio frequency current transformer (RFCT) type detector.
On the other hand, the advantage is that the pulse signals are usually much larger
because they can be placed closer to PD spots. The PD activity in each phase,
moreover, can be determined.
Figure. 3.2 Commercially available Epoxy-mica encapsulated couplers
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Currently Doble lemke (DN 50/80), and Omicron (UVS 610) uses this kind of sensors
on their UHF PD measurement for power transformer. Externally mounted UHF
sensors first developed as a GIS application and it has been widely used as
transformer UHF detection as well [63]. Detailed information about various UHF
antennas such as horn, loop, and, dipole type for GIS applications is described in [54].
Directional coupler [21, 60, 64-65]: The directional coupler is a combination of a
capacitive with an inductive sensor. It is possible to use two directional in a cable joint. By doing so, it is possible to distinguish PD impulses coming from outside (left
or right side) or inside the joint. In other cases, depending upon the direction of pulse,
energy can be coupled to a different output port in case of special sensors with two
outputs. The main application of this type of sensor is using a cable joint. For cable
joint application, a directional coupler can achieve high sensitivity. Typical operating
frequencies are usually from several MHz up to GHz.
3.2.2 Non-electric sensors
Fibre optic sensor: Detection of acoustic signal from a PD source can also be done
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by increasing positive hole density in a p-type semiconductor. This sensor is currently
only used in an academic field but its use has been shown in some papers. However
based on this sensor, off-line PD detection in GIS is possible.
Piezoelectric transducers [72]: The sensor is typically operating in the frequency
band in the 120 – 160 kHz range. In order to minimize the varying response according
to the electromagnetic fields, the transducer can be either a differential type utilizing
two crystals or a shielded single crystal transducer with an integral pre-amplifiercircuit. Usually an integral pre-amplifier circuit type is the more common
configuration due to high amplitude and low impedance output. Since the acoustic
impedance of a sensing crystal differs from as that of the steel transformer wall, an
efficient hard-epoxy resin material is used with thermal and electrical isolation
characteristic. Commercially available acoustic detection for PD localization which is
applicative for transformer has been successfully used.
3.3 PD monitoring visualization
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.
Figure. 3.3 PRPD patterns as pulses and pattern (PD-Smart)
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However, this PRPD pattern of each measurement cannot entail complete
identification of fault type because it depends on the PD measurement unit, sensor,
frequency band, test object and multiple causes of overlapping faults. Because of that,
there are some cases the typical patterns of PRPD do not match the true cause of PD
[76]. In order to increase accuracy of PRPD match with true fault causes, the same
measuring configuration and reference of each test object are required. A more
sophisticated display of PRPD in 3D in terms of PD amplitude, cycle number, and
phase position is shown in [77]. Pattern analysis and recognition based on PRPD will
be introduced in the on feature extraction and classification section.
3.3.2 Time resolved method
PD display based on measuring time can be called time resolved PD data shown in
Figure 3.5. Since this visualization focuses on more on the timing of PD occurrence,
time resolved data can provide information on the location of PD with several sensors
placed at different spots rather than PD magnitude. In [78], time of flight calculation
based on time resolved PD pattern at GIS is presented in detail in chapter 4. Other
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3.3.3 3-Phase Amplitude Relation Diagram (3 PARD)
3-PARD, or a star diagram, is cross talk between more than one phase on each
measurement [43, 81-83]. So called multi-terminal measurement, measuring 3 phases
with three couplers, can acquire synchronous PD data for all three phases of the test
object such as three phase transformer or GIS. This method make it possible to
compare the magnitude of PD occurrence on each phases, helping locate PD source
occurring in perhaps one of the three phases and eliminating external noise shown in
the display. The 3-PARD is a plot with a 120° phase shift of the three phase axis
shown in Figure 3.6. This method has been developed by the Technical University of
Berlin.
Figure. 3.5 3-PARD comparing PD magnitude on each phase [29]
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3.4.1 Noises in PD
Detecting of a true PD signal from measured results is a matter of in-depth knowledge
and incremented experience on measured signal and noise characteristic in different
situations and test objects. Since PD activities in power equipment occur within less
than a few hundred nanoseconds as fast rising time which is low level pulse
depending on faults type of the test objects, the de-noising process can be achieved by
understanding the noise characteristic and eliminating them from the true PD signal.
Typical noise during PD measurement can be categorized [85, 86].
Sinusoidal noise: This type of noise is the narrow band noise signal such as
communication carrier signal from AM/FM modulation which can be removed by
applying, for instance, a digital filter.
Pulse type (repetitive or random) noise: This type of noise possibly comes from power electronics, other switching operations or, Radio Frequency (RF) emissions
from power equipment. Even though repetitive noise can be rejected by a gating
circuit and other method which can detect periodic noise against PD signal, random
pulse type noise is hardly eliminated.
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Figure. 3.6 Principal of gating method for noise reduction
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Those two couplers detect signals at different spots with time difference for the same
PD signal which can be from the test object side not from a grid. Thus, by comparison
of pulse arrival time on two couplers, one can distinguish noise from the grid side.
The basic scheme is shown in Figure 3.9 [1, 88].
3.4.4 Digital filter method
When PD is corrupted by noise caused by radio communication, a matched filter is a
very well-suited as a solution. First of all, a matched filter can make it possible to
maximize SNR of PD by suppressing noise. In addition, it can make accurate
estimation on the time of arrival and magnitude of maximum PD pulse. The time of
arrival of PD pulse and SNR are deeply related as those two variables are inversely
proportional. Simply a matched filter uses a template which is a prediction of the
shape and amplitude of a PD pulse. The coefficients should be determined in order to
construct a specific matched filter for a specific measurement. One solution for this,
for example for the cable case, is the injection of a known pulse and measuring its
pulse propagation characteristic as impedance. By calculating time-of-arrival, the PD
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PD measurement, reducing noises and extracting a very small amount of data from
actual measurement [96]. The basic steps of wavelet transform applied for noise
reduction are described below.
Decomposition: set a mother wavelet and a maximum decomposition level,
computing the wavelet decomposition coefficients at each level from 1 to N.
Thresholding: Compute threshold coefficient for each and apply threshold to the
coefficients at each level
Reconstruction: Reconstruct the signal with the modified coefficients from 1 to N
3.4.6 Statistical method
Statistical methods for extracting PD features are based on PRPD pattern [73, 97-98].By applying statistical computation on PRPD patterns, different distributions can be
characterized as statistical parameters. The following distribution functions are used.
Skewness: shows the asymmetry or degree of tilt of the data of the distribution
compared to a normal distribution.
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Number of peaks: represents the distribution with single peak or more. The peak of
the distribution can be defined as:
1 1
1 1
0, 0i i
i i
dy dy
dx dx (3.3)
Where 1
1
i
i
dy
dxis the differential coefficient before and after the peak of the distribution.
Cross-correlation factor: shows correlation of the distribution shape between
positive and negative cycles of the distribution.
2 2 2 2
/
[ ( ) / ] [ ( ) / ]
i i i i
i i i i
x y x y ncc
x x n y y n (3.4)
where i x is the average discharge magnitude of positive half cycle and i y is the that
of negative cycle. When cc is close to zero, it means the shape of positive and
negative cycles are the same, otherwise it will be asymmetrical.
Asymmetry: shows the comparison of the mean level of the positive and negative
half of the voltage cycle.
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3.4.7 PD pulse shape method
This method is based on time resolved PD data for instance, apparent charge and
voltage magnitude within a certain time interval due to the fact that different PD
source can generate different PD pulse shape [73, 99]. The features extracted on an
one to one basis using single discharge source. The Following parameters can be used
Pulse rise time: time required to rise from 10% to 90% levels of the peak value.
Pulse decay time: time required to decay from 90% to 10% levels of the peak value.
Pulse width: time interval between 50% levels on both sides of the peak value.
Area under pulse: area enclosed by the q-t curve in the time interval for 10% levels
in the rising and falling segments.
3.5 PD pattern classification
Many researchers and theses have studied the pattern classification of PD. Therefore
many different methods have been introduced in order to understand and trace of
certain PD pattern such as artificial neural network, fuzzy logic, genetic algorism, and
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The optimal number of neighbours depends on the data. Thus if there is new data
coming to the feature space, it is classified by ―major voting‖ of k -number closest
neighbours of the new data spot. This also can be a drawback because certain types of
classes with the more frequent examples tend to dominate and are highly possible to
be selected. In order to overcome this problem, the class should be weighted by
experts or based on experience. The mathematical explanation is in [103]. The
advantage of this classification is easy to update new data to reference and, it is
simple to implement because it do not require training. However if redundant features
concerning the classification are included, possible errors can occur [104]. Therefore
careful selection of the feature is of importance.
3.5.2 Neural Network (NN)
Artificial neural network has been applied for PD classification [73, 97, 105-108].
The basic idea of NN is based on biological neural functions taken from brain-like
problem solving. The basic structure of NN consists of three mutually connected
different types of layer, an input layer, hidden layers, and output layer shown in
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classification. Details of both methods are presented in the above references.
Although NN is a very efficient tool for PD pattern classification especially due to the
fact that it does not requires any assumption the PD data structure, it has several
drawbacks including; dependence of convergence criteria upon learning coefficient
such as the number of layers, learning time; and it is also difficult to include new
features which requires retraining.
3.5.3 Support Vector Machine (SVM)
Support vector machine is one of the most promising techniques works by using
outstanding learning algorithms especially in power systems such as load forecasting,
power stability, and fault location detection [100, 109-110]. The main idea of SVM is
to calculate the optimal hyperplane separating two classes. SVM uses the so-called
non-linear kernel trick. SVM can find the solution of non-linearly separable condition
using an implicit mapping technique into a high dimensional dot-product space called
the feature space through the use of the kernel trick. A detailed explanation of the
kernel method is shown in the above references. Despite the sophisticated procedure
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3.5.4 Pulse Sequence Analysis (PSA)
Pulse Sequence Analysis (PSA) proposed by Martin Hoof and Rainer Patsch in 1990sis one of the most popular techniques for visualization of PD pattern classification
[111-113]. The idea of this method is that two consecutive pulses caused by PD
activities have a strong relationship. This means that the previous PD pulse has an
impact on the condition of next pulse. Therefore analysis of the relationship of
continuous pulses of voltage change due to the corresponding change of the localelectric field at the PD spot is an important factor which can investigate correlations
between consecutive pulses as shown in Figure 3.12.
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Figure. 3.123 Example of PSA in GIS; surface and corona discharge [113]
The advantage of PSA is its clear differences between certain PD patterns due to the
physical characteristics of PD activities according to the source of PD. However if the
voltage differences of continuous PD activities cannot be defined from measurement,
PSA is hard to apply
3.6 Signal processing of PD signal
Since measured PD signal has a very low magnitude happening with nano-second
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CHAPTER 4
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4.1.1 Transformer in power system
The transformer is one of the most complicated structured components in the powersystem. Normally most transformers operate efficiently for between 20-35 years,
which can be extended with proper maintenance [114]. Moreover, even though the
failure rate is quite low about 0.2-2% a year [115], it usually causes cascading faults
on different system components. Therefore, appropriate maintenance based
monitoring while in operation is the key point for preventing transformer failure.
Transformer insulations and its characteristics are also a bit complicated compared to
that of other components. The most common insulation material in transformer is
mineral oil which is being replaced by environmentally friendly oil and cellulose
[116]. In [33], failure rates according to the transformer parts are tap changer (41%),
windings (19%), tank and oil (13%), terminal (12%) and so forth. Another statistical
survey for transformer rate is shown in [117]
Transformer structure and failure rate
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Continuous PD monitoring on a transformer
According to the CIGRE data [117, 118], the tap changer has the highest possibility of
failure and then leakage, winding etc. That means appropriate healthy monitoring of
transformer can prevent possible failure beforehand. One recent paper demonstrates
on load tap changer monitoring using a continuous DGA method [119]. When it
comes to PD monitoring on transformer, it can be categorized as electrical, acoustical,
and chemical detection [44]. Regarding the electrical signal detection method, both
IEC 60270 and UHF detection is widely used. Due to the complexity in transformer
and its bulky volume, electrical sensors can be mounted outside on a bushing (IEC
60270) or in side of the transformer using the oil drain valve (UHF). For locating PD
course inside of the transformer, the acoustic emission method is used to calculate the
time difference between different sensor placements [120, 121]. Chemical detection
has been widely used in a periodical way with techniques such as DGA or Furan
analysis.
4.1.2 PD types in Transformer
In some papers [4, 23, 122], there are different types of PD in the transformer which
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coupling. The main reason behind this discharge is a bad earth connection in a
transformer
4.1.3 Different diagnosis and monitoring techniques on transformer
Among many monitoring techniques, this section only includes the on-line applicative
monitoring method and compatible with continuous PD monitoring on powertransformer. In this section, monitoring techniques are categorized as oil testing,
electrical, mechanical, and thermal monitoring of transformer. In [123-125], more
detailed transformer diagnosis and monitoring techniques are covered
Oil testing
Oil is one of the widely used insulation materials for transformer. An Oil test is
carried out by analyzing gases produced by local thermal stress or partial discharge
taking place in the insulation liquid during abnormal operation. Therefore, gas
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MVA rating transformers and a portable detector is not so precise compared to that of
one in a laboratory. However there have been many studies on this method including
combining artificial neural network and expert knowledge [126].
Furan Analysis and Degree of Polymerization (DP)
When the paper insulation in transformer lose the insulation strength, furanic
compounds that are by-products from paper insulation material appear in the oil,
which can be analyzed and used for paper aging prediction and DP. 2-furaldehyde is
considered the main product of aging, initiated by 5- furaldehyde in the early stages
[128]. Furan analysis is applied in the case of high level of thermal stress,
overloading, detection of high levels Carboxide, or sudden changes in oil color and
moisture content rates in the oil [114]. Life estimation of transformer according to the
DP is shown in [129]. The Constant K is defined as
( 237)
E
R T k Ae (4.1)
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13600
( 237 )0.004
=Estimated life of the transformer T e
A
(4.3)
Thermal monitoring
Thermal monitoring is a widely used method and has possible on-line applications.
High temperature means abnormal condition in any parts of a transformer losing
electrical dielectric strength if the thermal continues without any maintenance or
appropriate remedy actions. Usually thermal spots indicate possible faults and
insulation failures caused by overloading or local overheating which can accelerate
insulation aging rapidly. Because the transformer is complex equipment which has
non-linear characteristics with different components such as winding, load tap
changer, and core, thermal monitoring are not so precise to pinpoint the exact failure
spots which may be inaccessible to an external probe [116]. Infrared scanning check
of the external temperature on the transformer is now available [114]. One of the
disadvantages is that this method costs a lot in order to sense temperature directly
using fibre optic [116, 2]
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Mechanical Monitoring
Mechanically, On Load Tap Changer (OLTC) is the part where many faults occur.
Moreover, winding and core vibration can be detected by vibration sensors on the
transformer wall. This vibration signature can be analyzed by Fourier or Wavelet
transform. For visual inspection, checking of the pump isolation valve and oil flowing
indicator should be performed in order to confirm oil circulation. Plus, the conservator
breather also should be checked for the correct oil level. Fan and radiators should be
kept clean in order to cool the transformer down.
4.1.4 On-line PD monitoring on transformer
On-line PD monitoring on transformer mostly uses the electrical detection method todecide PD occurrence, and the acoustic detection method to locate the PD source
inside the transformer. Especially before installation, PD monitoring can be used for
new transformers in order to find any possible manufacturing problems [132]. In this
section, promising PD monitoring techniques using different methods, sensors,
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noise, appropriate signal processing techniques will be required for continuous on-site
PD monitoring.
Figure. 4.1 IEC 60270 recommendation for PD monitoring system on bushing
aC =The test object capacitance
1C =The HV capacitance of the bushing
2C = The LV capacitance of the bushing
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Figure. 4.2 Drain valve type UHF sensor [43, 136]
Figure. 4.3 UHF dielectric window type [137]
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In Figure 4.4, possible sensor place using Cartesian coordinates is shown. The biggest
problem of the AE detection method for localizing the PD source in the transformer is
its signal sensitivity. This method should measure acoustic signal at the same time
with at least 3 or 4 different sensors in different positions. In [121], detailed
mathematical explanations and possible signal processing techniques are covered.
4.1.5 Available products for on-line PD monitoring of transformer
Doble Lemke
Doble Lemke GmbH uses a conventional and unconventional method for on-line PD
monitoring of the transformer. In conventional PD monitoring, tap bushing coupling
with a low voltage capacitor is used as a sensor. This method is also applicative for
multi terminal measurement analyzed by a 3-PARD diagram eliminating noises and
comparing each phase. For the noise reduction, gating from a gate sensor and the
winding technique for phase-locked noise is used. In unconventional PD monitoring,
oil drain valve type sensor in the UHF (300 MHz-1 GHz) band or a UHF tap hatch
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Power diagnostix System GmbH
Power diagnostix System GmbH uses the conventional method using capacitive tap
on the bushing and the PRPD visualization method.
PowerPD, Inc.
PowerPD, Inc uses electrical sensors (clamp-on type HFCT) and acoustical sensors on
the transformer wall for on-line PD transformer monitoring. The sensitivity of the
sensors is 5pC and 20pC respectively. This system is fully compatible with SCADA
and remote accessibility.
Qualitrol Company LLC
Qualitrol Company LLC uses an unconventional PD monitoring method by attaching
rod type, window type, or drain valve type and hatch installation UHF sensors from
three to six around the transformer orthogonally on the side and top wall. This method
provides digital and analog output for web based or SCADA (supervisory control and
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4.1.6 Summary and Conclusion
Since transformer is the most intricate power system component, there are many
different ways or monitoring techniques for preventing possible faults. As some
companies have already provided on-line PD monitoring system on power
transformer for a couple of years, one can infer the fact that on-line PD monitoring of
transformer will be widely used in the very near future. Especially transformer
application PD monitoring techniques can be combined with other chemical,
mechanical or thermal monitoring with the other methods mentioned in this section.
On-line PD monitoring on the transformer focuses preliminary on PD magnitude
(peak value) and source location. Regardless of the apparent charge or UHF
measurement, changing or increasing of PD magnitude inside the transformer means
the fact that the transformer needs a more specific inspection or to be repaired.However, for the purpose of on-line monitoring, the UHF method is more reliable due
to the strong resistance to back ground noise. For the localizing of the PD source,
acoustic emission detection technique has the key solution of locating PD source
inside the transformer as highlighted in many papers.
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been widely used in the laboratory, on-site as the form of on-line or off-line
monitoring. Especially after installation of the cable system in the power system,
detecting faulty connection by different PD monitoring methods such as Damped AC
(DAC), Very Low Frequency (VLF) for example have been gaining its reputation.
Therefore, in this section, all kinds of PD monitoring techniques in cable will be
covered with detailed information regarding on-line PD monitoring in the cable
system as well as its available products in the market
4.2.1 Cable system in power system
Cable network systems in the power system are one of the most important part but
also the part most vulnerable to failure. Cable network can be categorized as Extra
High Voltage (EHV), High voltage (HV), Medium Voltage (MV) and Low Voltage
(LV) networks. The failure rate of the cable system is more frequent for lower voltage
networks, meaning LV networks have the greatest outage time of all network. More
than half of cable failure stems from electrical reason and the rest of them are due to
external non-electrical inference [132] In particular in MV networks the causes of
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Figure. 4.5 XLPE cable structure [142]
Components Description
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Continuous PD monitoring on cable network
Traditionally, PD monitoring on cables has been widely used due to its effectiveness
in CBM based monitoring and in localizing of the faults area. Especially for PD
monitoring of cables, standard procedures such as Very Low Frequency (VLF),
Damped AC (DAC), Alternative Current (AC) or Direct Current (DC) testing are
popular due to the fact that they can verify possible faults areas of joint and
termination after assembling by detecting and localizing PD in the cable. However,
for the purpose of on-line monitoring, high attenuation of the PD signal along the long
cable line makes it difficult to pick the exact PD and its location. Nowadays on-line
PD monitoring of the cable has been used by sensing the PD signal with HFCT,
capacitive coupling sensors, and so on. More detail will be covered up in the coming
section.
4.2.2 PD types in cable system
PD occurrence in the cable system can be divided into an internal, surface, and
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Corona: PD occurs in open air around the cable.
4.2.3 Different diagnosis and monitoring techniques on cables
There are many ways to monitor cables in a laboratory, on-site, or with on/off-line
methods. After a brief explanation of different monitoring techniques used on cable
networks, this section focuses on electrical, especially partial discharge method. Aswell as methods presented here, there are also destructive methods such as Cable
sampling, lead sheath analysis, and paper analysis [147].
Tangent Delta (Loss angle, or Dissipation Factor testing) Measurement
This method provides information regarding the aging of a cable by determining the
loss factor due to the tangent delta value which is related to the composition of the
connection, the trajectory, and the actual cable temperature. In perfect conditions, a
cable has capacitive characteristics maintaining the phase difference between voltage
and current at 90 degree. However, if there are defects in cable, the angle between
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appropriate temperature sensors. Semiconductor type sensor or optical fibre is popular
for continuous temperature monitoring of cables. The advantage of this monitoring is
to have real-time thermal behaviour of the cable that it is possible to use for thermal
rating re-assessment. However almost all cable systems are in operation practically at
low load condition for most of their service time, making it impossible to calculate
effective thermal resistivity of the cable. Therefore, temperature monitoring on a
cable should focus on a particular time and section of the cable.
Partial discharge monitoring
Even though thermal stress has a significant impact on the aging mechanism of the
cable, electrical stress is prominent cause of aging. PD monitoring of the cable is the
most effective method that is able to monitor electrical aging [153]. For localization
of PD, Time Domain Reflectometry (TDR) which uses the reflection of pulse signal at
the cable termination [154, 155] has been used. PD monitoring of the cable system
can be clearly categorized into the off-line and on-line method. Regarding the off-line
method, it has been widely used with an extensive voltage withstand test in order to
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On-line monitoring Off-line monitoring
Advantage
• Can be performed while cable is inoperation
• Economical• Real operation condition can be taken
into account.
• Proven technology for on-site,laboratory test, and commissioning
• High sensitivity• Calibration possible
Disadvantage
• Low sensitivity• Complicated data analysis is required• Insulated earthing ground is required
•
Out of connection is required• Relatively bulky equipment required• Outage cost• PD occurrence can be differ compared
to its operation at service voltage• Overall condition during testing
(Temperature, humidity, vibration)can differ from operation condition
Table 4.3 On-line versus Off-line PD monitoring on Cable [157, 158]
b. On-line PD monitoring
An on-line cable PD monitoring technique has proven its efficiency recently. As
mentioned above, on-line PD monitoring on the cable network has many advantages
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(FTRC) and, Inductively Turned Resonant Circuit (ITRC) with the following resonant
equation (4.4) in cable.
1
(2 ) F
LC (4.4)
L= test inductance (or external inductor)
C= cable capacitance
The FTRC method uses power electronics converter generating harmonics and noises
in the test system. Therefore appropriate signal processing techniques are required.
However, there is no moving part included in this method. On the other hand, because
ITRC usually use auto transformer, there are no such electronic pulse noises.
Moreover voltage can increase smoothly which makes it easier to reach the PD
inception voltage. The drawback of ITRC is its moving components which should be
maintained periodically.
Damped AC (DAC) voltage method: This method consists of a direct voltage source,
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Advantages of this method are its simplicity, lightweight and cost effectiveness with
low power required
Impulse voltage method: Impulse voltage with a very fast rate of rise and decay rate
similar to power frequency can be applied for on-site tests. This method has its
strength owing to lightweight equipment. Disadvantages of this method are hard to
determine the inception voltage of PD, high attenuation along the long cable length,
distance dependent test results, and difficulty to find correlation between routinefactory and on-site test regarding partial discharge values.
4.2.4 On-line PD monitoring on cable
IEC 60270 method is not appropriately applicative for on-line PD monitoring on
cables. Usually HF or UHF detection for gaining high signal to noise ratio (SNR) is
an attractive method for this purpose [163-169]. Since cable terminal and joint is the
part of cable most vulnerable to failure, on-line PD monitoring on cable accessories is
important for cable monitoring.
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Figure. 4.7 HFCT coupling application at the cable termination [168]
In Figure 4.7 HFCT in a slightly different location at the cable terminal is shown.According to the availability, the coupling spot can be adjustable. In order to localize
the PD source in the cable system, dual sensor techniques (installing two sensors at
each end of cable or cable joint) are required. Because of strong attenuation along the
cable, PD localization requires more engineering techniques such as the pulse
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Emerson
Their approach for online cable PD monitoring combines Tangent Delta testing, off-
line method with VLF PD monitoring, and the ultrasonic method. RF embedded noise
reduction can eliminate noise from PD with RFCT as a sensor. Regarding localization
of the PD source, they can make it possible to have about 1 % accuracy in up to 3
miles of cable length, which is an application for XLPE, EPR, PILC and CLX
Armored cable types
HVPD
HVPD uses HFCT attached around the earth connections and TEV attached
magnetically to the outside of metal-clad switchgear sensor which is applicable for
Polymeric (XLPE, PVC), Paper (PILC, MIND), Rubber (EPR), both 3-Core and
Single-Core Cables, and 'Mixed' cables with transition joints. Two cable ends attached
sensors are monitored for PD localization using a pulse injection method which is
successfully performed for up to 5 km on MV cable
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EPR). 3D visualization of PD in the cable moreover makes it possible to check PD
occurrence according to the cable length, time and intensity.
Power PD
Power PD uses HFCT as a sensor on shield ground cables which can be shown as a
PRPD or 3D graph.
Techimp
Techimp uses HFCT sensors, and FMC (Flexible Magnetic Coupler) sensors directly
at the two terminations of the cable. In long cables, the installations can be performed
at the middle of cable. For localization of a PD source, they analyze Amplitude/
Frequency characteristics of PD, TDM method, and Arrival Time Analysis with GPS
(Global Positioning System). Moreover this can be connected to a Ethernet network,
and controlled from a remote location.
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thus the higher frequency pulses related to PD activity is only detected near the PD
source.
The most appropriate sensor selection for cable case is capacitive coupling and HFCT
according to the application. Since cable accessories, joint and terminal, are the
biggest cause of possible faults, on-line PD monitoring near joint or terminal of cable
has been widely used. However, using two HFCT at each end of cable with PD
localizing techniques by TDR or pulse injection method has been proven its efficiencyon on-line PD monitoring for long length cable.
4.3 Rotating Machine
Rotating machine such as synchronous generator, induction motors and DC or AC
machines is one of the most important parts of the power system. The main reasons of
faults in rotating machines are thermal, electrical and, mechanical stress. Continuous
PD monitoring of rotating machine has been considered as efficient diagnostic tool for
several decades [170 171] In this section on line PD monitoring of rotating machine
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Subassembly Component Materials
Enclosure
•
Enclosure• Heat exchanger
electricalconnections
• Bushings• Bearings
• Fabricated structural steel• Steel, copper or brass tube• Cast epoxy resin• Steel Babbitt, high tensile steel rolling elements or soft bearing alloy on bearing shells
Stator body
• Frame•
Core• Core clamp
• Structural steel•
Electrical steel laminations• Structural steel or non-magnetic, low-conductivity alloy
Stator winding
• Conductorsinsulation
• End Windingsupport
• Hard drawn copper or copper wire• Mica-paper, glass or film impregnated with resin• Glass fibre structural materials and impregnated insulation
felt, ropes and board
Rotor winding
• Conductors• Insulation• End winding
support
• Hard drawn copper or copper wire• Mica-paper, glass, or film impregnated with resin• Impregnated glass fibre rope
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rate comes from persistent overloading (4.2%), and normal deterioration (26.4%). The
main failed components on RM are stator ground insulation (23%), turn insulation
(4%), and others (8%) [175,176]. More detailed information regarding RM failure is
in [177]. Therefore PD monitoring stator windings has normally been performed in
many industries and utilities. Continuous PD monitoring provide several advantages
for rotating machines; (i) provides warning for personnel, and (ii) solves the problem
of difficulty for RM testing under the same condition by supplying continuous
trendable data [174]. Moreover, other stress such as thermal or mechanical vibration
on RM can create a void or cracks which are detectable in the form of PD, expressed
as a symptom of stator winding failure [178].
4.3.2 PD types in rotating machines
The most popular sensing place for PD monitoring on RM is at the machine terminal.
However PD can occur inside of RM usually from stator winding which can be
attenuated or distorted during propagation from the PD source to the measuring place.
Therefore analysis of the magnitude and wave form of PD sometime provides
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delaminated at the copper conductor due to the thermal overstressing [181]. This
depends on the thermal condition of RM [182]
Endwinding Discharge: This usually occurs in the overhang region when a
contamination of the conductor takes place owing to mechanical corrosion or for
particular RM, where bar coils belonging to different phases locate in the same slot
[183]. Therefore the reason of this discharge results from phase to phase voltage with
not enough room between coils of different phases or partly conductive contamination[76]. According to [182], this type of PD usually has a high magnitude in negative
cycle and it is temperature dependant.
4.3.3 Different diagnosis and monitoring techniques on rotating machines
In [184], an intensive review of almost all possible monitoring techniques with regard
to RM is covered in detail. Largely, there are thermal, chemical, mechanical and,
electrical monitoring techniques have been widely used.
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• Use thermal imaging, to find a hot spot in the RM, which has been used on a
lot of other HVE.
• Evaluate distributed temperatures of RM or bulk temperatures of the coolant
fluid.
Chemical monitoringHigh thermal stresses in RM generate chemical reactions in the insulation material,
usually starting from 120 Celsius by emitting hydrocarbons and ethylene. However,
this method tends to be expensive to perform and is limited by its accuracy.
Electrical monitoring [185]
With regard to electrical monitoring on RM, methods include the insulation resistance
and polarization index, partial discharge, Capacitance and Dissipation Factor, Motor
Current Spectral Analysis (MCSA), High Voltage DC Ramp and Power Monitoring
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DC due to the cancellation of AC components from different phases if the flux and
torque is in a normal condition [186].
Motor Current Spectrum Analysis (MCSA) monitors stator current and its
spectrum. This can be easily implemented with Current Transformer (CT) around
supply cables. Because its accurate analysis and easy installation, this method has
been widely used.
Partial Discharge can be applied in two different ways, on-line and off-line. In the
case of off-line, just like off-line PD monitoring after laying the cable case, high AC
test voltage is fed into the cable and PD occurrences are recorded. Off- line PD
monitoring on RM which are not in operation are analysed without any operating
stress such as thermal or mechanical vibration, and other possible stresses while the
machine is in the grid. This information can mislead or failure to notice possible faults
in RM during operational condition. However, on-line PD monitoring on RM can
provide realistic data under the same circumstances of real conditions and situations
of load variation. In particular, on-line PD monitoring on RM largely depends on
operation temperature and load condition. One limitation of PD monitoring on RM is
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measurement sometimes accompanies its temperature and load record, for example, a
full load at a moderate temperature.
Figure. 4.8 Capacitive coupling method on RM [110]
For noise reduction, two sensors installed at different spots in one phase terminal can be used. The basis of this method is the arrival time difference between two sensors.
By doing so, sensors can recognize the PD signal source from an external or internal
spot.
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SSC is about 50 cm long and 1.7mm thick as shown in Figure 4.9. SSC has two
coaxial cable outputs at one end which can be connected to a signal collector located
on the outside of the generator. Typically six to nine SSC can be installed on one
generator. The advantages of this type of connection are noise immunity from stator
winding ends or other external sources, and a PD pulse detection ability from 1 to 5
nanosecond in stator winding.
4.3.5 Available products for on-line PD monitoring of RM
Doble Lemke
Doble Lemke installs capacitive coupling at the generator‘s bus bar of each phase.
The sensors cover the frequency range according to IEC 60270 and VHF. In order toeliminate noises, gating antenna detecting noise signals are attached, for instance,
grounding of the machine enclosure is used. By using PRPD analysis, the signal is
interpreted and identified in terms of each phase
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Power Diagnostix GmbH
Power Diagnostix GmbH installs a capacitive coupler close to windings such as a bus
bar at each phase if necessary especially for large machines. Global intranet access
and visualization of the monitoring data can be connected to this installation.
PDtech (Qualitrol Company LLC)
PDtech uses capacitive couplings near the generator terminal and HFCT around the
cable which is available for all HV-machines rated current and voltage. This
application provides an alarm and is compatible with SCADA systems.
PowerPD
They capture PD signals from generators and motors by coupling in each phase using
a Capacitor Coupler. This application has an early warning system and scans between
200 KHz-300 MHz.
T hi
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vulnerability to faults, the PD signal from stator winding can be distorted or
attenuated at the sensing spot.
For online PD applications, capacitive sensors at the generator terminal, Rogowski
coils and HFCT at the end cable connection spot, or directional electromagnetic
coupler at stator winding slot wedge has pr