An investigative study on the influence of correlation of PD statistical features on PD pattern recognition

Abduallhi Abubaker  Mas'ud; Brian G. Stewart

doi:10.1109/ICD.2018.8514755

An investigative study on the influence of correlation of PD statistical features on PD pattern recognition

Abduallhi Abubaker Mas'ud, Brian G. Stewart

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

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Abstract

This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.

Original language	English
Title of host publication	2nd IEEE International Conference on Dielectrics (ICD) 2018
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Number of pages	5
DOIs	https://doi.org/10.1109/ICD.2018.8514755
Publication status	Published - 1 Nov 2018
Event	2nd IEEE International Conference on Dielectrics - Budapest, Hungary Duration: 1 Jul 2018 → 5 Jul 2018 Conference number: 2nd

Conference

Conference	2nd IEEE International Conference on Dielectrics
Abbreviated title	ICD 2018
Country/Territory	Hungary
City	Budapest
Period	1/07/18 → 5/07/18

Keywords

partial discharge
ensemble neural network
correlation coefficients

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Mas-ud-Stewart-IEEE-2018-An-investigative-study-on-the-influence-of-correlation-of-PD-statisticalAccepted author manuscript, 553 KB

Cite this

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title = "An investigative study on the influence of correlation of PD statistical features on PD pattern recognition",

abstract = "This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.",

keywords = "partial discharge, ensemble neural network, correlation coefficients",

author = "Mas'ud, {Abduallhi Abubaker} and Stewart, {Brian G.}",

note = "{\textcopyright} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2nd IEEE International Conference on Dielectrics , ICD 2018 ; Conference date: 01-07-2018 Through 05-07-2018",

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Y1 - 2018/11/1

N2 - This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.

AB - This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.

KW - partial discharge

KW - ensemble neural network

KW - correlation coefficients

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DO - 10.1109/ICD.2018.8514755

M3 - Conference contribution book

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PB - IEEE

CY - Piscataway, N.J.

T2 - 2nd IEEE International Conference on Dielectrics

Y2 - 1 July 2018 through 5 July 2018

ER -

An investigative study on the influence of correlation of PD statistical features on PD pattern recognition

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