on line pd monitoting of power system components

8/11/2019 On Line PD Monitoting of Power System Components

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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 MASTERS 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 Hyvnen

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 Hyvnen, 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 Familyin 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

kC Measuring Capacitor

1U Applied test voltage

2U Voltage drop across the PD source

3U Voltage drop across the mR

mC Measuring capacitor

mR Measuring resistor

sG Grounding switch


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0R Reading of the PD instrument

0q Known calibrating charge

iP The probability of appearance for that value ix 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

ix The average discharge magnitude of the positive half cycle

iy 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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initialDP Initial DP

e Eulers 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) ............................................................................. 453.5.3

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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CHAPTER1


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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. 1.1TEAM 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 sensors 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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CHAPTER2


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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 mR


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3 1( )

a

a m

CU 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 aCby 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 ( 1fand 2f ) 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< 1f


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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

mR = Measuring resistor

mL = 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 ( kC ) can be expressed according to theIEC 60270 as shown in below.

0 0.1 ( )a kC 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

kq 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 (30300MHz) O O O

UHF (300M3GHz) O O -

Table 2.1 Suitable frequency band according to system components (O=Good, =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


High sensitivityLocation of PD

is assessable(insome case)

Test is possiblefor impulsevoltagecondition


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 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 currentpulse ( Electromagnetic wave)Acoustic, Chemical by products,Optical spectrum

Measuring systemCoupling device, transmissionsystem, measuring instrument

Sensing components, transmissionpath, 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 monitoringmeans 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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CHAPTER3


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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 cablejoint. 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 120160 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 frompower 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 ix is the average discharge magnitude of positive half cycle and iy 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.

3.5PD 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

3.6Signal processing of PD signal

Since measured PD signal has a very low magnitude happening with nano-second


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CHAPTER4


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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 Tk Ae (4.1)


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13600

( 237 )0.004

=Estimated life of the transformer Te

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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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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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

EconomicalReal operation condition can be taken

into account.

Proven technology for on-site,laboratory test, and commissioning

High sensitivityCalibration possible

Disadvantage

Low sensitivityComplicated data analysis is requiredInsulated earthing ground is required

Out of connection is requiredRelatively bulky equipment requiredOutage costPD occurrence can be differ compared

to its operation at service voltageOverall 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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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 Dischargecan 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 canbe 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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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 proven efficient in research and available

products in the market. VHF is the most appropriate monitoring frequency range. In

order to reduce noise and locate the PD source, two sensors at one phase different

spots, pulse shape analysis with 3 PARD diagram can be used.

4.4 GIS (Gas Insulated System)

GIS has been widely used for HV insulation since 1960. Due to its high insulationcharacteristic and break down voltage with injected gas- usually SF_6 compared to air,

GIS makes it possible to construct the substation in a more compact and reliable way

[192]. PD detection techniques in GIS are conventional, unconventional or combined

both covered in a recent paper [193]. Usually the sensor should be located within an

i t di t th t th d t t PD i l f th GIS UHF


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Gas

Usually Use as gas insulation material or Mixture with [195]High pressure (4 bar) and low pressure (1.2bar)

High pressure has better dielectric characteristics

Conductor system

Aluminum tubes according to the rated voltage and current, its thickness anddiameter can be specified

Silver plated contact surface

Solid Spacer

Physical support of high-voltage conductors and mechanical operation ofswitchgear

Cause electrical field distortion within GIS

Table 4.5 GIS's insulation and enclosure components and material [141]

The inside components of the GIS usually vary between types, such as the circuit

breaker, disconnection switch, current transformer, voltage transformer, bus bar and

so forth [141]. Therefore, different components can cause different failures inside the

GIS. In [196], different analysis of GIS failures was conducted based on thirty years

failure history from five German utilities and 7 companies. Depending on the location

of GIS, the most common failures occurred in the switching compartment (40.4%),

Voltage Transformer (VT), Surge Arrestors (SA) and bushing compartment (17.3%)


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Electromagnetic Wave (TEM). This wave cannot pass through the opened contact

switching compartment. At higher power frequencies, however, electric and magnetic

fields of the transmitting waves are not entirely transverse to the direction of wave

propagation, known as Transverse Electric (TE), Transverse Magnetic (TM). The

waves have short wave lengths compared to TEM, and either electric or magnetic

fields can have the components transmitting at the same direction toward wave

propagation direction, which can pass though the opened contact in GIS. The cut-off

frequency between TEM and TE, TM can be defined by the following equation

( )c a b (4.5)

Where c = cut-off wave length, a, b= outer and inner radius of the conductor and

chamber respectively. For example in the case of 420kV,cis about 1.2m so that the

cut-off frequency can be approximately 250MHz. This TEM and TE, TM can affect

the detectable frequency range for on-line PD monitoring on GIS. Appendix A in

[200], detail mathematical frame work of TEM, TE, and TM is covered.


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Free moving particle: This is a particle freely moving inside the GIS. Poor

manufacture processes and contact between different electrodes inside GIS can cause

free moving particles. Even though free moving particles have been found to be

harmless compared, for example, to floating electrodes, continuous PD occurrence

from free moving particle can result in SF_6 decomposition, and eventually

breakdown in the GIS [203]. This was pointed out as the main cause of failure in a

GIS [204]

Particles fixed on the spacer or insulation surface:This case indicates that certain

free moving particles can be fixed on the resin spacer or other insulation surface

inside the GIS. Fixed particles can generate corona type PD or breakdown in the gas-

solid interface by accelerating electric field distribution strength at that location [205].

4.4.3 Different diagnosis and monitoring techniques on GIS

According to [206], indicative methods to find possible faults are PD diagnostic

(22.4%), Visual Inspection of switchgear enclosure and surrounding area (18.1%),

and Thermo-graphic Inspection (12%). Therefore PD monitoring and visual


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operation of GIS. The UHF/AE PD detection method has been regarded as the most

promising PD monitoring technique for GIS. Detailed techniques in terms of on-line

monitoring are covered in this chapter.

High frequency current detection [209]:Current pulses caused by discharge in GIS

can be detected using by capacitor or charged conductor and intermediary insulator

equipped with a receiver electrode immersed in resin. This method has been used and

is a possible application for permanent monitoring.

SF6 quality assessment [209]: Impurities of GIS cause a significant impact on

insulation failure. Therefore, appropriate monitoring for SF6 quality has been used

periodically in order to decide dielectric strength. Gas analysis performed by sampling

for gas-chromatography or infer-red spectrograph is a proven method. Air contents

measurement with a portable oxygen detector can give an immediate indication of air

contents in the GIS. Lastly continuous or periodic moisture measurement is efficient

as well. Because this method is relatively expensive and redundant, periodic detection

for the first month of operation is sufficient in order to ensure SF6 filling condition in

the GIS.


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Unconventional method [196, 210]

Unconventional on-line PD monitoring on GIS uses VHF/UHF detection and the AE

detection method. As mentioned above, since TEM is highly attenuated at higher

frequencies, TEM mode in GIS is strong in VHF (40 to 300 MHz). Even though the

VHF method has many similarities with UHF in the sense that they can provide the

location of PD, the most significant disadvantage of VHF is interference similar to

IEC method. However, VHF method sometimes makes it possible to be calibrated by

injecting a known pulse. On the other hand, the UHF (300 to 3 GHz) method can

detect TE and TM which are generated by a very fast rising time PD current within

ten picoseconds. In addition UHF has a similar sensitivity to the IEC method due to

its good immunity to noise. The rule of thumb regarding distance between UHF

sensors should be within 20m. The most widely used sensor types are in Figure 4.10.


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PD localization [135]

PD source localization inside the GIS simply uses time-of-flight measurement with

two different sensors. Electric PD signal inside the GIS can propagate two UHF PDsensors at different time intervals according to the sensor placement.

Figure. 4.11 Time of flight method for PD localization in GIS [135]

In Figure 4.11, the simple scheme of PD localization is shown. If there are two

different UHF sensors and the PD occurs between them, the time domain PD location

can be calculated as below in a situation when the time of flight at two different UHF

sensors is known.

8/11/2019 On Line PD Monitoting of Power System Compon

on line pd monitoting of power system components

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