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Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors

Internal partial discharges taking place inside an insulation has been identified as one of the major contributors of the high voltage equipment failures. Recently, with the rapid development of computer based signal processing, it is possible to identify the source of partial discharge (PD) signals...

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Main Authors:	Md Thayoob Y.H., Ghani A.B.Abd., Ghosh P.S.
Other Authors:	6505876050
Format:	Conference paper
Published:	2023
Subjects:	Classification (of information) Digital signal processing Electric cables Electric loads Electric transformers Fast Fourier transforms Frequency domain analysis Pattern recognition Soil testing Spectrum analysis Statistical tests Thermal conductivity Time domain analysis Voltage distribution measurement Partial discharges (PD) Short time fast fourier transforms (STFFT) Electric breakdown
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id	my.uniten.dspace-29861
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spelling	my.uniten.dspace-298612023-12-28T16:57:59Z Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors Md Thayoob Y.H. Ghani A.B.Abd. Ghosh P.S. 6505876050 24469638000 55427760300 Classification (of information) Digital signal processing Electric cables Electric loads Electric transformers Fast Fourier transforms Frequency domain analysis Pattern recognition Soil testing Spectrum analysis Statistical tests Thermal conductivity Time domain analysis Voltage distribution measurement Partial discharges (PD) Short time fast fourier transforms (STFFT) Electric breakdown Internal partial discharges taking place inside an insulation has been identified as one of the major contributors of the high voltage equipment failures. Recently, with the rapid development of computer based signal processing, it is possible to identify the source of partial discharge (PD) signals originating from complex insulation structures under varied operating conditions. The research on PD pattern classification is mostly performed in the time-domain but there is also an increasing trend to undertake frequency-domain analysis. In this research work, an experiment has been carried out on an 11KV, single-core 240mm2 XLPE cable. The operating conditions are varied using two parameters which are the soil condition in terms of soil thermal resistivity and cable loading in terms of core temperature. The time-domain electrical PD signals obtained from a PD measurement system are transformed into frequency spectrum by short-time fast Fourier transform (STFFT) using Matlab toolbox. In order to carry out the PD pattern classification, nine frequency-domain statistical descriptors are identified for the present work. A database for the range of values of the nine descriptors have been developed using PD signals obtained from one of the soil thermal resistivity at normal full load working temperature that is identified as standard. The values of the descriptors obtained from signals corresponding to other operating conditions stated earlier are then compared against the standard database and the number of successes is measured in terms of the commonly used recognition rate. The analysis of the results has clearly indicated that the proposed methodology has the ability to classify PD patterns originating from the cable under varied operating conditions. Final 2023-12-28T08:57:59Z 2023-12-28T08:57:59Z 2003 Conference paper 2-s2.0-1542747992 https://www.scopus.com/inward/record.uri?eid=2-s2.0-1542747992&partnerID=40&md5=59d347f64f495538ef5b7fb39ec76204 https://irepository.uniten.edu.my/handle/123456789/29861 171 175 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/
topic	Classification (of information) Digital signal processing Electric cables Electric loads Electric transformers Fast Fourier transforms Frequency domain analysis Pattern recognition Soil testing Spectrum analysis Statistical tests Thermal conductivity Time domain analysis Voltage distribution measurement Partial discharges (PD) Short time fast fourier transforms (STFFT) Electric breakdown
spellingShingle	Classification (of information) Digital signal processing Electric cables Electric loads Electric transformers Fast Fourier transforms Frequency domain analysis Pattern recognition Soil testing Spectrum analysis Statistical tests Thermal conductivity Time domain analysis Voltage distribution measurement Partial discharges (PD) Short time fast fourier transforms (STFFT) Electric breakdown Md Thayoob Y.H. Ghani A.B.Abd. Ghosh P.S. Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors
description	Internal partial discharges taking place inside an insulation has been identified as one of the major contributors of the high voltage equipment failures. Recently, with the rapid development of computer based signal processing, it is possible to identify the source of partial discharge (PD) signals originating from complex insulation structures under varied operating conditions. The research on PD pattern classification is mostly performed in the time-domain but there is also an increasing trend to undertake frequency-domain analysis. In this research work, an experiment has been carried out on an 11KV, single-core 240mm2 XLPE cable. The operating conditions are varied using two parameters which are the soil condition in terms of soil thermal resistivity and cable loading in terms of core temperature. The time-domain electrical PD signals obtained from a PD measurement system are transformed into frequency spectrum by short-time fast Fourier transform (STFFT) using Matlab toolbox. In order to carry out the PD pattern classification, nine frequency-domain statistical descriptors are identified for the present work. A database for the range of values of the nine descriptors have been developed using PD signals obtained from one of the soil thermal resistivity at normal full load working temperature that is identified as standard. The values of the descriptors obtained from signals corresponding to other operating conditions stated earlier are then compared against the standard database and the number of successes is measured in terms of the commonly used recognition rate. The analysis of the results has clearly indicated that the proposed methodology has the ability to classify PD patterns originating from the cable under varied operating conditions.
author2	6505876050
author_facet	6505876050 Md Thayoob Y.H. Ghani A.B.Abd. Ghosh P.S.
format	Conference paper
author	Md Thayoob Y.H. Ghani A.B.Abd. Ghosh P.S.
author_sort	Md Thayoob Y.H.
title	Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors
title_short	Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors
title_full	Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors
title_fullStr	Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors
title_full_unstemmed	Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors
title_sort	partial discharge pattern classification using frequency-domain statistical descriptors
publishDate	2023
_version_	1806426573218250752
score	13.222552

Partial Discharge Pattern Classification Using Frequency-Domain Statistical Descriptors

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