CHAPTER 2

Partial Discharge Measurement,

Acquisition and Experimental Studies

28

CHAPTER 2

Partial Discharge Measurement, Acquisition and Experimental Studies

2.1 Partial Discharge- Characteristics and Types of Sources

IEC 60270 Standard on Partial Discharge Measurements defines Partial Discharge [7]

as ‘a localized electrical discharge that only partially bridges the insulation between

conductors and which may or may not occur adjacent to a conductor’. Stated otherwise,

the electrical breakdown phenomenon confined to the localized regions of the insulating

medium between two conductors at different potentials is called Partial Discharge (PD).

PD phenomena are inherently self-quenching mechanisms which may be ascribed to

the fact that the electric field intensity below a certain point is extremely low to sustain

continued growth of the discharge. This aspect may also be attributed to the space charge

(memory propagation effect) [63] formed during the discharge process which initiates

reduction of the local electric field intensity that is insufficient to sustain the discharge.

PD is also inherently a stochastic process which exhibits substantial statistical variability

in its major characteristics such as pulse amplitude, pulse shape and time of occurrence.

A few important factors that may influence the stochastic nature of PD include the

availability of the initiatory electron in the gap, memory propagation due to the effect of

residual charge (space charge) from previous pulses, role of irradiation, growth of

discharge-induced cavities etc wherein each factor may be interrelated. Further, the

29

presence of memory propagation effects makes PD phenomenon a complex non-

Markovian process imposing restrictions on the capability to predict and diagnose PD.

PD may be broadly classified based on the mechanism of discharge and site of

location of discharge [4] as internal PD, which occurs inside the dielectric and external

PD, which takes place at the interface or on the periphery of the dielectric. In general

there are five types of PD namely, (1) Internal discharges occurring in gas filled cavities

and oil filled cavities leading to breakdown (2) Surface discharges occurring in gases or

in oil in the presence of electrical stress component parallel to the surface of the dielectric

(3) Corona discharges occurring at sharp points protruding from electrodes in gases and

in liquids (4) Electrical trees originating from conducting particles, electrodes or from

cavity in solid insulation and (5) floating part discharges occurring in badly grounded

components in a High Voltage (HV) test circuit.

2.2 Methods for Partial Discharge Measurement and Their Relevance to Pattern

Recognition

Though several attributes of PD such as magnitude, rise time, recurrence rate, phase-

relationship, time interval between successive pulses, discharge inception and extinction

voltage etc characterize its occurrence, the more appropriate features from the viewpoint

of pattern recognition and also as represented in a majority of modern digital PD

measurement and acquisition systems are the phase angle of occurrence of PD pulses (φ),

magnitude of the apparent charge during discharge (q) and the discharge rate (n).

30

IEC 60270 offers essentially three modes of performing studies on PD testing and

measurement namely straight (direct) detection, polarity discrimination and balanced

bridge method. The first method is usually appropriate when the testing is carried out

under controlled laboratory testing condition wherein substantially lesser influence of

electromagnetic interference and background noise is ensured. The latter two techniques

are usually resorted to when appropriate mechanism for reduction and mitigation of noise

has to be ensured for improved signal to noise ratio. Though these schemes offer better

mechanisms for countering noise during measurements the first method is more

commonly used in practice and is more appropriate in the context of this research since

detailed study and analysis of PD signatures is carried out in appropriately shielded

controlled laboratory environment. Figure 2.1 shows the general test arrangement using

IEC 60270- Method 1.

Figure 2.1: Typical PD Measurement and Acquisition Test Setup (Ct - Equipment Under Test, Cc- Coupling Capacitor, Cp- Reference Pulse Calibrator, Z and Cq- components of the Quadrupole Arrangement)

31

From the context of obtaining the quasi-integrated PD pulses, coupling devices

(commonly referred to as quadrupole) comprise band pass filters of either wide band or

narrow band characteristics. Since identification of the pulse polarity is closely related to

the identification of the source of PD, it is evident that the appropriate choice of the

quadrupole becomes vital. However, in this research, detailed studies and analysis carried

out on the role of the polarity of the pulses in classification capability of the proposed

hybrid system of PNN clearly indicates that the classification capability is not seriously

compromised. This aspect is evident and may be attributed to the fact that the Gaussian

kernel utilized in the PNN modular versions is compact, positive and serves as an

excellent optimal basis function in the least square sense and hence normally distributes

the noise input during fitting of the data.

2.3 Fabrication of Benchmark Laboratory Models- Single and Multiple Sources

The laboratory models are fabricated to replicate the PD patterns that are

representative of the source of discharge displayed in the oscilloscope in line with the

recommendations of [64, 65]. This aspect is a basic yet vital requirement since

repeatability and reliability of the PD signatures obtained which are characteristic

features for a particular PD source would in turn serve as a mechanism to correlate and

validate the effectiveness of the test methodology in addition to providing a high

confidence level of the results interpreted during measurements and discrimination using

AI tools. Hence, the focus of this research is on appropriate validation of the

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methodology with divergent types of benchmark models rather than providing inferences

for complex real-time insulation.

The dielectric material used for the analysis of discharge signature patterns in this

research pertaining to internal PD including that of treeing mechanism is a synthetic

polymer ‘poly methyl methacrylate- PMMA’ (commonly referred to as perspex® or

plexiglas®). Though this material has been primarily used in this research due to its visual

(transparent) characteristic, it is worth mentioning that PMMA is characterized by

excellent optical and weather resistant properties. In addition it has been observed by

several researchers [66] that the time to breakdown characteristics of PMMA is relatively

good. Additional major characteristics of PMMA includes high arc resistance (hence

providing plausible application in high voltage circuit breakers), decreasing permittivity

with increasing frequency etc. During the course of the experiments, the test object was

placed in transformer oil bath to prevent surface discharges and external flashovers.

2.3.1 Experimental Study 1: Laboratory Models simulating Multiple Source PD Patterns

12mm thick, 80 mm diameter perspex with electrode-bounded cavity of 2 mm depth

simulates internal PD while a similar setup with gliding discharges is replicated to

replicate surface discharges. Figure 2.2 (a) and Figure 2.2 (b) depicts the arrangement of

the models.

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Figure 2.2: Laboratory Benchmark Model representing (a) Electrode Bounded Cavity (b) Surface Discharge

Corona discharge in air is replicated with a point electrode initiated from a stainless

steel rod with an approximate angle of 85° and a plane electrode of 10mm thickness and

60mm diameter. A similar arrangement immersed in transformer oil replicates oil-corona

discharges. Figure 2.3 (a) and Figure 2.3 (b) depicts the models.

Figure 2.2: Laboratory Benchmark Model simulating (a) Air Corona and (b) Oil Corona

HV

10mm Diameter, Polished Sharp Needle

Earth Electrode

Tip Radius: 100 μm

Perspex Non- Metallic Chamber

Perspex Non- Metallic Chamber

HV

10mm Diameter, Polished Sharp Needle

Tip Radius: 100 μm

Earth electrode

Transformer Oil

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Electrode bounded cavity with air-corona is produced by inserting a needle

configuration (2 mm) from the high voltage (HV) electrode in addition to a 2 mm

electrode-bounded cavity on perspex in the HV electrode. Multiple electrode bounded

cavities replicate with nine numbers of 2mm and 4mm holes. Figure 2.4 represents the

replicated multiple source PD models. Figure 2.5 portrays a snapshot of the reference

models utilized for analysis.

Figure 2.4: Laboratory Models simulating Multiple Source PD (a) Electrode bounded cavity with air- corona (b) Multiple Cavity discharges

Figure 2.5: Laboratory models replicating Corona (Air and Oil), Electrode-bounded cavity, Multiple Cavity and Electrode Bounded Cavity with Corona

2.3.2 Experimental Study 2: Laboratory Models simulating PD initiated Electrical Treeing

PD initiated electrical treeing studies have been carried out by several researchers

since this category is one of the most important phenomena that describe degradation in

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electrical insulation system. Since patterns of PD before and after treeing initiation are

complex and appear visibly similar, the focus of this study is on ascertaining the ability of

the proposed dynamic modular PNN version in discriminating the stochastic PD patterns

before and during treeing.

2.4 Preparation of Industrial Objects for PD Testing and Pattern Recognition

2.4.1 PD Pattern Recognition approach for Pollution Severity Initiated Flashover in

Ceramic Insulators

Contamination flashover has become a vital aspect in the design of high-voltage

outdoor insulation. Hence, this research involves conducting studies pertaining to severity

associated flashover prediction in ceramic insulators using PD signature patterns as a

technique for diagnosis. Since this research focuses on conducting predictive tests to

determine the performance of polluted insulator due to dynamic changes in the PD

pattern for varying salinity, an artificial pollution test is conducted with equivalent salt as

the pollutant to analyze the performance of the insulators. Since the clean fog test [42]

reflects the contamination mechanism occurring in industrial locations, testing is carried

out by dipping the insulator into slurry consisting of 40g of kaolin with varying levels of

salinity (39 gm/l, 57 gm/l, 67 gm/l and 91 gm/l). It is observed during studies that except

for the clean and dry case of leakage current waveforms, the remaining cases are similar

in spite of the varying polluting conditions. On the contrary, in the case of PD patterns, it

is revealed that there exist significant differences between patterns that either affect or do

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not affect insulator flashover performance and arcing due to polluted surfaces. It is

observed that PD on polluted surfaces clearly exhibits a larger distribution with much

scattered number of PD pulses as compared to dry and wet insulator before the initiation

of scintillation led flashover. A few researchers have also reported on similar study [72-

74] that clearly delineates the importance of similar studies.

2.4.2 PD Pattern Recognition in Cross Linked Polyethylene (XPLE) Cables and

Distribution Transformer Bushings

Extruded power cables are formed by polyethylene (PE), cross linked polyethylene

(XPLE), ethylene propylene rubber (EPR) etc. Since, the advantage utilizing the PE

polymer to form cross-linked structure for better strength and increased operating

temperature limits have enabled power utilities to prefer XPLE power cables in most

electrical installations, detailed PD measurement and recognition studies have been

carried out on 33kV extruded XLPE power cable that simulate some of the significant

and typical categories of installation defects. PD related failure in power cables are

generally categorized into internal cavity discharges, surface (gliding) discharges along

interfaces due to missing semi-conductive screen, corona discharges as sharp protrusions

at end terminations and electrical treeing. The presence of protrusions on the semi-

conductive screen which are pointed in the radial direction to the cable conductor leads to

a major critical situation due to high intensity electrical stresses. Surface discharges at the

interface between air and XLPE insulation due to missing semi-conductive screen may

lead erosion culminating in degradation.

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Hence, for the purpose of investigations four major types of artificial sources that

represent the occurrence of PD in 33kV, single core XPLE power cable samples namely

electrode bounded cavity (between the semi-conductive screen), surface discharges

(along the interface between air-XLPE insulation), corona discharges (due to sharp

protrusion at the end termination) and multiple source cavity with surface discharges is

taken up for analysis.

Electrode bounded cavity defect is replicated by forming a flat cavity of 5mm

diameter and 2mm depth formed on the surface between the XLPE dielectric and the

semi-conducting layer. The cavity is formed by removing a small part of the outer semi-

conductive screen and by making the cavity of the appropriate dimensions and then

covering the area with an aluminium tape. In addition, the measuring electrode is covered

with a grounded aluminium strip to ensure good screening during measurement. Figure

2.6 shows the schematic arrangement of the cable taken up for PD measurement.

Figure 2.6: Typical Sample of Cable simulating Electrode-bounded Cavity Defect

The artificial defect simulating surface discharges due to missing semi-conductive

screen is created by removing a small segment of the outer semi-conductive screen of the

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cable as shown in Figure 2.7. This type of defect is most prevalent during installation of

cable and cable jointing since the discharges are produced due to electric field

enhancement at the edge of the semi-conductive layer leading to gliding discharges.

Figure 2.7: Cable Sample replicating Gliding Discharges due to Missing Semi-

Conductive Layer

Multiple sources pertaining to one major form of the complex fully overlapped PD

signature i.e., cavity with surface discharge is replicated as shown in Figure 2.8.

Figure 2.8: Cable Sample depicting Multi-Source (Electrode-bounded Cavity with

Surface Discharge) Defect

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Detailed studies have also been carried out on various sources of PD on 11kV high

voltage bushings of a 315 kVA, 11kV/ 433V, Dyn11 distribution transformer at varying

applied voltages that replicates real-time external PD sources such as gliding discharges

due to pollution, corona discharges and partially overlapped multiple sources defects

(gliding discharges with air-corona).

2.5 Laboratory Experimental Setup for PD Pattern Recognition Studies

Investigations have been carried out using a 10kVA, 100kV, 50Hz test transformer

(MWB make) with a W.S Test Systems make (Model No.: DTM-D®) Digital PD

Measurement and Acquisition System measurable in the range 2-5000pC. A Tektronix

built-in oscilloscope (TDS 2002B) alongside a tunable adjustable filter-insert (Model:

DFT-1) with a variable center frequency of range 600 kHz- 2400 kHz at a bandwidth of

9 kHz is utilized for acquiring the appropriate PD pulses. Facility is provided to display

the measured PD pulses either in pico-coulomb (pC) or in milli-volt (mV) in accordance

with stipulations laid down by IEC 60270. The direct detection and measurement test

setup as recommended in IEC 60270 is utilized in carrying out the studies in this research

work since the tests have been carried out in a controlled laboratory environment thus

obviating the need for alternative strategies for noise suppression. Notwithstanding, the

PD measurement and acquisition system comprises window gating facility to mask and

suppress unwanted background noise during measurement. Further, a 1nF coupling

capacitor is also included in the test setup to improve the transfer characteristics of the

test circuit. Calibration of the test setup is carried out using a reference calibrator (Model:

40

PDG®) and in line with the requirements of IEC 60270. Figure 2.9 shows the PD

measurement and acquisition system used in this research.

Figure 2.9: Digital PD Measurement and Acquisition System and a typical display of PD patterns on a sinusoidal time base for air-corona discharges 2.5.1 Experimental Test setup for Laboratory Models simulating Multiple Source PD

Patterns

Figure 2.10 and Figure 2.11 depicts the typical test arrangement utilized for carrying

out PD pattern recognition studies on laboratory benchmark models. It is worth

mentioning that the tests were performed under controlled laboratory conditions with

appropriate shielding of the laboratory test setup including that of the control panel along

with the measurement system.

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Figure 2.10: Typical Test Arrangement depicting PD pattern recognition system for Laboratory Models

Figure 2.11: Snapshot depicting the typical experimental layout for PD studies on Laboratory Benchmark Models

Quadrople Calibrator

Coupling Capacitor

Test Transformer

Test Object

42

2.5.2 Experimental Test setup for Laboratory Models replicating studies for PD initiated

Electrical Treeing

This research envisages studies based on samples comprising virgin moulds of

perspex blocks of 80mm diameter and 12mm thickness. The high voltage electrode

comprises a stainless steel needle of 1mm thickness with a tip radius 500μm inserted to a

depth of 4mm by hard pressing. The sample is placed in a test cell containing transformer

oil to prevent flashover. Figure 2.12 and Figure 2.13 replicates the test setup utilized for

carrying out electrical treeing studies.

Figure 2.12: Typical Test Arrangement depicting PD pattern recognition system for Electrical Treeing Studies

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Figure 2.13: Snapshot of the Typical Test Arrangement utilized for Electrical Treeing Studies

2.5.3 Experimental Test setup for PD Pattern Recognition for Pollution Severity initiated

Flashover in Ceramic Insulators

A series of experiments are performed on four insulators of varying dimensions and

salinities. Measurement of correlated PD and leakage current were studied for the

following cases: 1. pin of the insulator, 2. cap of the insulator and 3. pin and cap of

insulator. The generic test setup for acquiring PD signatures for analysis is shown in

Figure 2.14.

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Figure 2.14: Generic layout of the test Arrangement depicting PD pattern recognition system for Prediction of Flashover due to Pollution Severity

2.6 Partial Discharge Test Measurement and Data Acquisition

2.6.1 Experimental Study 1: Measurement and Data Acquisition for Laboratory Models

simulating Multiple Source PD Patterns

The signature analysis was carried out after due verification of patterns acquired for

reference benchmark models. These patterns were verified for their appropriateness with

the reference patterns given in IEC Guidelines [8] and [64]. Figure 2.15 Figure 2.16,

igure 2.17and Figure 2.18 learly demonstrate the non-stationary behaviour of PD

signatures due to various sources of discharges for varying applied voltages.

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Figure 2.15: Typical Single Source PD Signature of Air Corona Discharges

Figure 2.16: Single Source PD Signature of Single Electrode Bounded Cavity

Discharges

Figure 2.17: Multiple Source PD Signature-Electrode bounded Cavity with Air Corona

46

Figure 2.18: Single Source PD Signature of Surface Discharges in Air (gliding

discharges)

Acquisition of PD data is carried out initially for small sets of patterns due to both

single and multiple PD sources. The fist data comprises a total of two sets of training

database i.e. 20 and 25 sets. A total of 52 PD fingerprints samples are indicated in Table

2.1. The second study comprises large database PD patterns of defects for varying

applied voltages as indicated in Table 2.2. It is pertinent to note that the patterns acquired

exhibit the statistical variations in the pulse patterns for each cycle of the sinusoidal

voltage that display the inherent non–Markovian nature of PD making the task of

classification difficult. The task becomes even more challenging due to varying applied

voltages. In order to ascertain the capability of the modular PNN versions in classifying

PD signatures that exhibit non-stationary and complex stochastic behaviour, preliminary

studies in each case has been conducted by digitally superimposing PD signatures

obtained for single source defects on similar lines with [40], since these patterns may

result in even more onerous challenges during recognition of partially and fully

overlapped patterns. Detailed analyses have clearly indicated the robustness of the

scheme in discriminating digitally superimposed signatures also. This aspect is reported

based on this research study in [118] and deliberated in detail in Chapter 5, Section 5.2.

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Table 2.1: Small Dataset PD Signatures for Benchmark Laboratory Models

PD Category

Type of PD Label for Classification

No. of PD Patterns

1 Electrode Bounded Cavity EC 8 2 Surface Discharge S 10 3 Oil-Corona OC 10 4 Air-Corona C 6 5 Electrode Bounded Cavity with

Corona ECC 8

6 Bounded Cavity with Surface Discharge

VS 10

Table 2.2: Database pertaining to different PD Sources for Various Applied Voltages

PD Category Type of PD Applied Voltage

(kV) Total No. of

Patterns

1

Electrode Bounded Cavity

7.28 120 9.1

9.6

2

Air-Corona 13.65 120 20.93

22.75

3 Oil-Corona 20.93

120 29.12 31.85

4

Electrode Bounded Cavity with Air-Corona

9.1 120 9.6

13.6

5 Multiple Electrode Bounded Cavity

9.1 120 9.6

13.6

2.6.2 Experimental Study 2: Measurement and Data Acquisition for Laboratory Models

simulating PD initiated treeing patterns

As discussed in the previous sections, the fabricated sample is placed in a test cell

containing transformer oil in order to prevent surface discharges and flashover during

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testing and analysis. In addition, since it has been observed by a few researchers that the

signature patterns of PD before and during treeing inception are not quite distinctive, the

patterns are selected such that only after a threshold tree length [5] the patterns provide

distinct details regarding ‘no tree’ and ‘tree’ patterns thus enabling appropriate

identification. Figure 2.19 show photographs of the test arrangement and the nature of

treeing growth during studies.

(a) Branch-type dendrite treeing initiation (b) Branch-type tree growth Figure 2.19: Photographs of dendrite morphology propagation and growth in Electrical Treeing Studies

Studies have been carried out at an applied voltage of 23.5kV and a total of 200

datasets pertaining to PD and tree initiated patterns have been acquired. Since it has been

observed by researchers that the PD patterns and those pertaining to inception of tree are

not quite distinct, the patterns utilized for identification are so chosen that data are

obtained for ‘no tree’ and ‘tree’ patterns.

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2.6.3 Experimental Study 3: Measurement and Data Acquisition of PD Signature

Patterns for Pollution Severity Initiated Flashover Studies in Ceramic Insulators

Measurement of correlated PD and leakage current were studied for the following

cases: 1. pin of the ceramic insulator, 2. cap of the ceramic insulator and 3. both pin and

cap of ceramic insulator. Table 2.3 indicates the characteristics and details regarding the

studies conducted on individual disc insulators for varying salinities.

Table 2.3: Characteristics and Dimensions of Disc Insulator Samples

Sample No.

Characteristics and Dimensions Creepage Distance

(mm)

Diameter (mm)

Cantilever Strength

(kN)

Glazing Height of Cap

Diameter of Cap

Salinity (g/l)

Sample 1 340 265 45 Dark Brown

85 828 39

Sample 2 327 264 45 Light Brown

90 878 67

Sample 3 320 270 45 Dark Brown

90 873 57

Sample 4 295 265 45 Light Brown

104 968 91

Table 2.4 indicates study carried out for two sets of samples conducted on a single 12kV

insulator indicating the various stages of dry-band formation leading to pollution severity

initiated flashover studies.

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Table 2.4: Database of PD patterns during to pollution performance studies in ceramic insulators (Sample 2 and Sample 3)

Category of PD

Type of PD Applied Voltage (kV)

No. of Patterns

Sample 2 Sample 3

1

PD (wetted without Dry band arcing)

6.1 5.4 40

2

PD during Dry band (at pin end)

2.8 4.8 40 3.6 5.6

3 PD during Dry band (at cap end)

6.4 5.9 80 10.7 6.2

4 PD during Dry band (at pin and cap)

12.4 7.4 40 16.3 8.1

A few researchers have also reported on similar study [73] that clearly delineates the

importance of similar studies. Figure 2.20 clearly illustrates this aspect. Typical snapshots

of the studies carried out during pollution performance studies in insulators are depicted

in Figure 2.21.

Figure 2.20: PD and leakage current waveforms during Pollution Performance Test on Insulators under wet-condition with 67g/ l: (a) Applied voltage 16.6kV; (b) Applied voltage of 24.1 kV

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(a) Arcing at Cap (b) Arcing at Cap (c) Flashover Initiation (d) Flashover and Pin Figure 2.21: Typical Images of Tests on Insulators Prediction of Flashover due to Pollution Severity

2.6.4 Experimental Study 4: Measurement and Data Acquisition of PD Signature

Patterns for Multiple Source Defects in XPLE Power Cables and Transformer

Bushing

PD signature datasets obtained from various samples of cables and 11kV transformer

bushing is indicated in Table 2.5 and Table 2.6 respectively. Incidentally, it is significant

to note that the PD signatures have been obtained for two major forms of multi-source

defects namely the partially overlapped signatures (cavity with air corona) and fully

overlapped signatures (cavity with surface discharge) in order to ascertain the capability

of the proposed hybrid PNN variant (S-Transform versions) in providing solutions to

recognition of non-stationary PD pulse patterns.

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Table 2.5: Dataset of PD Signatures obtained from Samples of 33kV XPLE Power

Cable

Table 2.6: Dataset of PD Signatures obtained from 11kV Bushing of Distribution

Transformer

PD data acquisition is carried out using PD Gold® software developed by HV

Solution Inc, U.K. which is interfaced with the PD detection and measurement system.

PDGold©

data acquisition software provides facility to acquire high resolution PD signals

PD Category

Type of PD Discharge Inception Voltage

Applied Voltage (kV)

Total No. of Patterns

1

Electrode Bounded Cavity 2.9

3.2 150 3.6

4

2

Air-Corona 3.4

3.8 150 4.2

4.4

3 Surface Discharge 3.6

4 150 4.5

4.8

4

Electrode Bounded Cavity with Surface Discharge

2.8

4 150 4.4

4.8

PD Category

Type of PD Discharge Inception Voltage

Applied Voltage (kV)

Total No. of Patterns

1

Air-Corona 5.8

6.2 150 7.5

8.1

2 Surface Discharge 4.4

4.8 150 5.6

7.1

3 Air Corona with Surface Discharge

6 6.8

150 7.3 7.8

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at a sample rate of 1 sample per 2.5 nanoseconds (ns). The unit also detects PD for 50Hz

power cycle which allows the user to observe the shape of the PD pulses detected in

addition to acquiring the phase resolved PD (PRPD) patterns in real-time. The software

also provides a PD threshold level for recording the number of PD pulses (count) per

cycle [74]. The set-up of the unit for an on-line PD testing requires filling a user-friendly

‘Data Input Form’ with dropdown menus. The pulses are displayed in both sinusoidal and

elliptical forms selectable in auto or manual mode. In manual mode, the user may record

the data for a considerable period though the data acquired in this study is limited to

duration of 5-10 minutes. The set-up of the unit for an on-line PD testing requires filling

a user-friendly “Data Input Form” with dropdown menus as indicated in Figure 2.22. The

user fills the data input form with appropriate information and an automatic test report is

generated by the software for ease of recall.

Figure 2.22: Screenshot of Data Input Form during PD Measurement and Acquisition

54

The software acquires the PD pulses synchronously on a 50 Hz power cycle so as to

display the pulses in both sinusoidal and elliptical forms selectable in auto or manual

mode. In manual mode, the user may record the data for a period of 5-10 minutes which

is acquired from a minimum of 240 to a maximum of 750 waveforms per channel. The

recording can be stopped whenever necessary by clicking on the “Stop” button. Figure

2.23 shows typical PD pulses acquired during the testing, measurement and acquisition

process.

Figure 2.23: Typical snapshot of the output from Digital PD Measurement and Acquisition System of PD patterns on an elliptical-time base of electrode bounded cavity with air-corona Figure 2.24 and Figure 2.25 show the oscilloscopic display and the screenshot of the PD

plot respectively (φ -q and q-n) for the test carried out for a period of 5 minutes.

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Figure 2.24: Typical waveform representation on Oscilloscope depicting (a) Single Electrode Bounded Cavity with Air- Corona (b) Air-Corona on Sinusoidal Base

Figure 2.25: Typical Plot of PD patterns of φ -q and q-n exhibited by Single Electrode Bounded Cavity Benchmark Model

2.7 Summary

The salient characteristics, properties and terminologies pertaining to PD are

deliberated in detail for the context of PD pattern recognition. The role of the

appropriateness of the type of testing method in pattern recognition and its limitation is

explained. The need for validation of PD signatures acquired during testing and

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measurement, rationale behind the procedure of utilizing PD signature database based on

laboratory benchmark models for pattern recognition studies and its impact on

discrimination is summarized. Detailed specifications on the formulation of the test

circuit arrangement, experimental setup and procedure for data acquisition is discussed

from the point of view of pattern recognition and discrimination.

A few major aspects with regard to PD testing, measurement and acquisition are

summarized below:

1. Need for appropriate choice of PD measurement circuit among the various methods

recommended by IEC 60270 such as direct detection method, bridge detection method,

pulse discrimination method to suit laboratory or site conditions become vital during

measurement. In all cases appropriate PD calibration has to be carried out ensuring that in

each case the signal to noise ratio is high based on appropriate choice of the bandwidth of

tunable filter during acquisition.

2. Need to carry out proper cross- validation of the acquired PD pulse signatures for

various benchmark models in line with the recommendations given in [8, 64] since

patterns are observed to be highly non-Markovian and non-stationary in nature making

the task of discrimination difficult.