Classification of partial discharge sources using statistical approach

In high-voltage (HV) power equipment, degradation of insulation has been main concern for protection of equipment. This is due to occurrence of partial discharges (PD) activity within HV insulating systems which can be initiated from different types of local internal or external defects. Thus, parti...

Full description

Saved in:
Bibliographic Details
Main Authors: Ren L.W., Rahman M.S.A., Ariffin A.M.
Other Authors: 57194501910
Format: Article
Published: Institute of Advanced Engineering and Science 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-23214
record_format dspace
spelling my.uniten.dspace-232142023-05-29T14:38:29Z Classification of partial discharge sources using statistical approach Ren L.W. Rahman M.S.A. Ariffin A.M. 57194501910 36609854400 16400722400 In high-voltage (HV) power equipment, degradation of insulation has been main concern for protection of equipment. This is due to occurrence of partial discharges (PD) activity within HV insulating systems which can be initiated from different types of local internal or external defects. Thus, partial discharge (PD) identification and classification are important for diagnostic insulation systems problems in order to ensure maintenance process can be carried out effectively and hence improve reliability and durable operation of HV equipment. In this work, the relation of the observable statistical characteristics from PD data with the characteristic of the defect is an important factor to determine the defect inside insulation system. Ultimately, the statistical parameters obtained from PD data can be used to classify different PD sources occur inside HV insulation system. Thus, the objective of this paper is to produce a unique pattern according to discharge source using statistical method. Several statistical parameters such as mean, variance, standard deviation, skewness and kurtosis have been used and analysed. � 2017 Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T06:38:29Z 2023-05-29T06:38:29Z 2017 Article 10.11591/ijeecs.v6.i3.pp537-543 2-s2.0-85020480357 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020480357&doi=10.11591%2fijeecs.v6.i3.pp537-543&partnerID=40&md5=94b6e4ba6b4ce80612f28b89141a8eb0 https://irepository.uniten.edu.my/handle/123456789/23214 6 3 537 543 Institute of Advanced Engineering and Science Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description In high-voltage (HV) power equipment, degradation of insulation has been main concern for protection of equipment. This is due to occurrence of partial discharges (PD) activity within HV insulating systems which can be initiated from different types of local internal or external defects. Thus, partial discharge (PD) identification and classification are important for diagnostic insulation systems problems in order to ensure maintenance process can be carried out effectively and hence improve reliability and durable operation of HV equipment. In this work, the relation of the observable statistical characteristics from PD data with the characteristic of the defect is an important factor to determine the defect inside insulation system. Ultimately, the statistical parameters obtained from PD data can be used to classify different PD sources occur inside HV insulation system. Thus, the objective of this paper is to produce a unique pattern according to discharge source using statistical method. Several statistical parameters such as mean, variance, standard deviation, skewness and kurtosis have been used and analysed. � 2017 Institute of Advanced Engineering and Science. All rights reserved.
author2 57194501910
author_facet 57194501910
Ren L.W.
Rahman M.S.A.
Ariffin A.M.
format Article
author Ren L.W.
Rahman M.S.A.
Ariffin A.M.
spellingShingle Ren L.W.
Rahman M.S.A.
Ariffin A.M.
Classification of partial discharge sources using statistical approach
author_sort Ren L.W.
title Classification of partial discharge sources using statistical approach
title_short Classification of partial discharge sources using statistical approach
title_full Classification of partial discharge sources using statistical approach
title_fullStr Classification of partial discharge sources using statistical approach
title_full_unstemmed Classification of partial discharge sources using statistical approach
title_sort classification of partial discharge sources using statistical approach
publisher Institute of Advanced Engineering and Science
publishDate 2023
_version_ 1806426245810880512
score 13.211869