The application ò fuzzy entropy to selecting features of fantial deschange high voltage cable joents

The application ò fuzzy entropy to selecting features of fantial deschange high voltage cable joents

ISSN 1859-1531 - THE UNIVERSITY OF DANANG, JOURNAL OF SCIENCE AND TECHNOLOGY, NO 12(97).2015, VOL 1 33

THE APPLICATION OF FUZZY ENTROPY TO SELECTING FEATURES

OF PARTIAL DISCHARGE IN HIGH VOLTAGE CABLE JOINTS

Nguyen Tung Lam

The University of Danang, University of Science and Technology; tunglam87@gmail.com

Abstract - Partial discharge (PD) measurement is one of the most

important diagnostics methods of insulation systems in high

voltage equipment.PD activities may stem from various kinds of

defects, and its characteristics correspondingly behave differently

In this study, 104 features of partial discharge are collected through

a series of experiments in laboratory, which are large

dimensionality data set.However, not all of features are useful for

classification and recognition, so the problem needed to solve is

the selection of the relevant features and elimination of

non-important features.The fuzzy entropy algorithm was applied to find

out features owning characteristics for distinguishing the defects in

high voltage cable joints

Key words - high voltage cable joint, partial discharge, feature

selection, Fuzzy entropy, recognition

1 Introduction

Underground cables are a key link in metropolitan power

grids Hence, any cable accident can lead to serious

economic losses and disruption of service to customers

Despite the strict quality controls for the cable production

process in a plant, potential defects can occur in cable

accessories during installation [8] Although the degradation

mechanism and identification process of cable joints have

not been fully cleared yet, it is deserving to conduct an

investigation into the prevention of unexpected failure of

cable systems [6] Power cable system basically consists of

cables themselves and their accessories Cable accessories

include joints and terminations.Statistically, the accidents

caused by partial discharge mostly occur at cable joints [7]

Compared to many protection methods in power system,

partial discharge is considered as one of the most

promising measures for monitoring and detecting possible

faults in the system before they occur.One of the undoubted

advantages of a computer-aided measuring system is the

ability to process a large amount of information and

transform this information into an understandable output

[4] In this study, phase-resolved data are acquired from

digital PD measurement systems during tests The phase

resolved data consists of a 3D discharge pattern: phase

angle – discharge magnitude - discharge rate (q-φ-t) at a

specified test voltage

There are many kinds of defects in cable joints and each

defect own specific characteristics Different kinds of

defects create different partial discharge signs and the

extents of damage are not the same Based on the

investigation into partial discharge from defects, the type

of defects could be recognized, and from that the states of

cable joints can be evaluated appropriately.In this study,

104 features of partial discharge are collected through a

series of experiments in laboratory, which are large

dimensionality data set However, not all of features are

useful for classification and recognition, so the problem

needed to solve is the selection of the relevant features and

elimination of non-important features In addressing this problem, different methods of data reduction have been used and managed to eliminate the redundancy and non-important features present in the data sets Among them feature selection has been shown to be a powerful approach of dealing with high dimensional data by selecting relevant features from data set and at the same time removing irrelevant and redundant features that harm the quality of the results, and therefore builds a good learning model A good feature selection techniques will

be able to detect and model the noisy and misleading features from the domain problem and help to get minimal feature subsets but still keep the important information present in the original data [5] This research proposes Fuzzy entropy method to evaluate the contribution of each feature to classification It shows that not all the features have one and the same discriminatory power [1] As a result, the crucial features are identified by using fuzzy entropy

2 Partial discharge data acquisition and analysis

Partial discharge could be defined as an electrical pulse

or discharge in a gas filled void or on a dielectric surface

of a solid or liquid insulation system This pulse or discharge only partially bridges the gap between phase insulation to the ground, and phase to-phase insulation

A full discharge would be a complete fault between line potential and ground These discharges might occur in any void between the conductor and the ground The pulses occur at high frequencies; therefore, they attenuate quickly

as they pass through a short distance The discharges are effectively small sparks occurring within the insulation system Therefore, it can deteriorate the insulation and can eventually result in complete insulation failure

A set of PD measurement tests were carried out at the High Voltage Laboratory of National Taiwan University of Science and Technology (NTUST) based on the standard IEC60270 [3]

Attenuator

Limiting resistor

HV

V Cable

Voltage signals

Circuit protector

MD

Partial discharge signals

Figure 1 Experimental setup for PD measurement

34 Nguyen Tung Lam

2.1 Partial Discharge Data Acquisition

PD measurements were generated and recorded from

laboratory tests During the experimental process, all of the

measuring analog data was converted to digital data in

order to be stored in computer After that process, these

data was transformed into q-φ-t format (discharge

evaluation pattern) This data format is called as Phase

resolved partial discharge data Figure 2 illustrates general

PD data acquisition scheme

The most basic quantities of PD activities are apparent

charge, q, apparent charge number n, and phase position of

PD pulses with respect to the applied test voltage, φ,

interpretation purposes of the under test insulation system

Figure 2 A general PD data acquisition scheme

However, the above mentioned quantities cannot be

sufficient for a perfect diagnostics So, heuristically there

have been introduced lots of features derived from basic

quantities termed as deduced and statistical operators, which

can be used for defect identification and evaluation

Therefore acquiring PD data and extracting statistical feature

from acquired data benefit us for reliable PD monitoring

- Basic quantities, which are quantities observed

during one voltage cycle

- Deduced quantities, which are integrated values of

basic quantities from the first group observed

throughout several voltage cycles

- Statistical operators, which are operators for the

statistical analysis of the deduced parameters

This process data analysis can be a good indicator for

ambiguous PD patterns to diagnose as it presents

distinctive features of each PD defect pattern which has

been accumulated for a longer time than PD real-time data

For the convenience of statistical analysis, the 3D patterns

were decomposed into two 2D distributions by projecting

it into the two axes - phase and magnitude

Statistical analysis is performed separately for those two

distributions Also, statistical analysis is performed

separately for phase angles from 0 to 180° (“positive” PDs),

for phase angles from 180° to 360° (“negative” PDs), and

on the difference between positive and negative PDs For

each of the distributions, two types of statistics, names

amplitude statistics and shape statistics, are calculated The

statistical descriptors are mean, standard deviation,

skewness and kurtosis In addition, overall maximum

magnitudes of positive and negative PDs and discharge

phase region PD patterns are also calculated as features

To diagnose a fault from the PD data it first needs to be

transformed into a generic workable format One way of

displaying the data is to plot consecutive pulses generated

by a defect present in the insulation on a 3D phase-resolved pattern, representing a one second (40 cycles) snapshot of

PD activity This is achieved by plotting each pulse, or in the case of the IEC data the peak amplitude of the apparent charge, on a three-dimensional axis consisting of the pulse’s relative amplitude, the cycle number on which the pulse appears and the phase position of the pulse on the voltage cycle An example of a phase-resolved pattern, which represents three kinds of defect PD activities, can be seen in Figure 5 The pattern is in the form of a 40x600 matrix of floating points that represent the PD activity in

40 consecutive cycles across 600 phase windows of the voltage cycle; with the positive half cycle appearing first, between 0° and 180° and then the negative half cycle between 180° and 360°

Figure 3 Partial discharge signal measurement

Figure 4 3D q-φ-t transformation

Figure 5 Defect type A 3-D q-φ-t pattern

Using data in this form, the knowledge-based system offers an automated approach to defect classification and offers an explanation of the reasons for its conclusion This ability offers a physical explanation for the automatic classification sets.For further statistical analysis, the 3-D patterns are decomposed into two 2-D distributions by projecting it into the two axes - phase and magnitude Figure 6 shows 3D q-φ-t pattern decomposition that is

ISSN 1859-1531 - THE UNIVERSITY OF DANANG, JOURNAL OF SCIENCE AND TECHNOLOGY, NO 12(97).2015, VOL 1 35 transformed into 2-D distribution pattern that includes two

axes - phase and magnitude

Figure 6 3-D q-φ-t decomposition

Having basic PD quantities at hand, through statistical

operators, 104 statistical features (positive and negative)

were extracted from the four major PD quantities related to

phase and height distributions These 104 statistical

features are also called PD fingerprints

The PD-fingerprint in this work is a histogram

combination of statistical features of a PD signal The

shape of the histogram provides information about the

nature of the PD signal [9] The features of a histogram are

statistical characteristics, where the histogram is used as a

model of the probability distribution of a pattern These

statistical features provide us with the characteristics of a

PD pattern

Statistical methods for extracting PD features are based

on phase resolved PD patterns By applying statistical

computation on PD patterns, different distributions can be

characterized as statistical features The mean value,

standard deviation, skewness and kurtois values are

calculated according to statistical formulas Discharge

phase region is also calculated

2.2 Experimental setup

In this research, the experimental objects are cable joint

defect models Two types of relevant models are well

designed, based on investigations of numerous power

equipment failures Two defect types are described, as

follows:

1 Defect type A: remove a part of insulation According

to the criterion, the length of insulation of two cable sides

is the same and complies with standard In this type, a part

of one insulation side was cut out

2 Defect type B: gap between insulation, a hole made in

insulation belong to the part of cable inside the joint

3 Feature selection based on fuzzy entropy

Feature selection is a process of choosing small subset

of features out of a set of candidate features based on

certain criteria Feature selection plays an important role in

classification for several reasons First it can simplify the

model and in this way computational cost can be reduced

and also when the model is taken for practical use fewer

inputs are needed which means in practice that fewer

measurements from new samples are needed Second by removing redundant features from the data set one can also make the model more transparent and more comprehensible, providing better explanation of suggested diagnosis Feature selection process can also reduce noise and in this way enhance the classification accuracy The key of PD classification problem like in any classification systems is a set of high quality features These features should represent the characteristics of PD signals More importantly, these features must possess strong discriminant power so that the classifier designed based on those features can give desired performance Since PD is a stochastic process, namely, the occurrence of PD depends on many factors, such as temperature, pressure, applied voltage, and the test duration, and since PD signals contain noise and interference, PD measurements corresponding to different insulation conditions are almost indistinguishable, i.e., PD diagnosis is a complex classification problem Thus finding

a set of high quality features that give more accurate and reliable classification is even more critical in design of PD diagnostic systems

In this paper, Fuzzy entropy method is proposed to reduce dimension of partial discharge features

For a classification system, the most important procedure is partitioning the pattern space into decision regions Once the decision regions are decided, we can apply these partitioned decision regions to classify the unknown patterns The partition of decision regions is part

of the learning procedure or training procedure since the decision regions are decided by the training patterns In fuzzy entropy classifier, decision regions are enclosed by the surfaces produced from each dimension The surfaces are determined by the distribution of input patterns Entropy is a measure of the amount of uncertainty in the outcome of a random experiment, or equivalently, a measure of the information obtained when the outcome is observed [2] In this paper, a fuzzy entropy measure which

is an extension of Shannon’s definition will be proposed The fuzzy entropy can discriminate the actual distribution of patterns better By employing membership functions for measuring match degrees, the value of entropy not only considers the number of patterns but also takes the actual distribution of patterns into account The fuzzy entropy reflects more information in the actual distribution of patterns in the pattern space Since the fuzzy entropy can discriminate pattern distribution better, we employ it to evaluate the separability of each feature Intuitively, the lower the fuzzy entropy of a feature

is, the higher the feature’s discriminating ability is The procedure for computing the fuzzy entropy of each feature

is described as the flowchart in Figure 7 This process includes four main parts:

- Determine the number of intervals

- Determine the interval locations

- Assign a membership function for each interval

- Compute the fuzzy entropy of each feature via summation of the fuzzy entropy of all intervals

At first, assume the number of interval I equal to 2 which

36 Nguyen Tung Lam

is the smallest number of interval Then increase I until the

total fuzzy entropy of I intervals is less than that of I - 1

intervals The final fuzzy entropy is computed with I-1

intervals

Set initial number

of interval I=2

Set initial centers of

intervals c

Assign interval label to

each element

Recompute the

cluster centers

Centers of intervals

are determined

No Yes

Assign membership

function for each

interval.

Compute the total

fuzzy entropy of

all intervals

I=I+1

I=2 I>2

False

The number of

interval I=I-1

& The fuzzy

entropy is

computed with I-1 intervals

True

Check: Does any center

change?

the total fuzzy

entropy of I intervals

1 intervals

Figure 7 Flowchart of calculating value Fuzzy entropy

4 Results

As mentioned in the previous chapter, each defect was

tested on 3 cable joints

Table 1 Set of PD data’s class

Remove a part of insulation A (A1, A2, A3)

Gap between insulations B (B1, B2, B3)

After finishing all the tests, partial discharge data of

defects was collected In this study, data obtained in the

results of experimental works was considered and

transformed into statistical features and these

considerations are explained in PD Data Acquisition and

Analysis section These statistical features including

skewness, kurtosis, standard deviation, mean, DPR,

〖 Q〗_sum,〖 Q〗_num,〖 Q〗_max,〖 Q〗_ave… are all

calculated based on the PD signals Statistical features

consist of 104 features numbered from 1 to 104 In this

study, 34 kV PD experiment data that include 120 sample

data for each defect model is used Apply Fuzzy entropy

theory to all features of partial discharge, with inputs to

Matlab program as values of all features and types of

defects corresponding As a result, the fuzzy entropy value

of each feature is computed; features with higher fuzzy

entropy are less relevant to classification goal Totally 9

pairs of defects (each pair includes 1 defect type A and 1

defect type B) were conducted to calculate fuzzy entropy

values Table 2 shows the features owning smallest values

of fuzzy entropy of each case

Table 2 Values of Fuzzy Entropy

No

No

No

No

No

No

No

No

No

As a result, it can be clearly seen that the feature number 2, 45 and 46 are always in the top of features having the smallest values of fuzzy entropy As mentioned

in the theory, those features impact significantly on classifying defects in cable joints We can use three features instead of all 104 features to recognize not only more accuracy but also less time of computing

Table 3 Selected features

2 Total number of partial discharge in all circles

45 Height distribution average partial discharge

ISSN 1859-1531 - THE UNIVERSITY OF DANANG, JOURNAL OF SCIENCE AND TECHNOLOGY, NO 12(97).2015, VOL 1 37 magnitude – kurtois values

46 Height distribution average partial discharge

maximum– kurtois values

5 Conclusion

Based on the preprocessing stage, it is necessary to

gather the database from conducted PD tests with two high

voltage cable joints including prefabricated defects

Phase-resolved PD data was successfully evaluated and processed

The data gathered from the selected databases is connected

to MATLAB software where the data is processed The

second part of the analysis system is based on feature

selection algorithm This takes Fuzzy entropy method as a

medium to process the PD data Feature selection techniques

were applied to the PD data and the characteristic points of

interests are being selected by computing fuzzy entropy

value of each defect The data provides meaningful

information for the classification of PD defects

The measure of uncertainty is adopted as a measure of

information Hence, the measures of fuzziness are known

as fuzzy information measures The measure of a quantity

of fuzzy information gained from a fuzzy set or fuzzy

system is known as fuzzy entropy In this study, the fuzzy

entropy algorithm was applied to find out three features

owning most useful characteristic for distinguishing the

defects in high voltage cable joinst

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(The Board of Editors received the paper on 07/10/2015, its review was completed on 09/27/2015)