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Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

Magnetic sensor is a relatively new method to collect time-resolved partial discharge (PD) signals in XLPE cables. This paper proposes a simple yet effective method to recognize patterns of PD signals obtained from the magnetic sensor. The technique consists of wavelet transformation to de-noise the...

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Main Authors:	Tho N.T.N., Chakrabarty C.K., Siah Y.K., Ghani A.B.Abd.
Other Authors:	54584502600
Format:	Conference paper
Published:	2023
Subjects:	neural network partial discharge pattern recognition statistical method time-resolved signals wavelet de-noising Backpropagation Feature extraction Magnetic sensors Partial discharges Pattern recognition Pattern recognition systems Statistical methods De-Noise Feature extraction methods Multi layer perceptron Partial discharge signal Time-resolved Wavelet denoising Wavelet transformations XLPE cables Neural networks
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id	my.uniten.dspace-30427
record_format	dspace
spelling	my.uniten.dspace-304272023-12-29T15:47:43Z Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals Tho N.T.N. Chakrabarty C.K. Siah Y.K. Ghani A.B.Abd. 54584502600 6701755282 24448864400 24469638000 neural network partial discharge pattern recognition statistical method time-resolved signals wavelet de-noising Backpropagation Feature extraction Magnetic sensors Partial discharges Pattern recognition Pattern recognition systems Statistical methods De-Noise Feature extraction methods Multi layer perceptron Partial discharge signal Time-resolved Wavelet denoising Wavelet transformations XLPE cables Neural networks Magnetic sensor is a relatively new method to collect time-resolved partial discharge (PD) signals in XLPE cables. This paper proposes a simple yet effective method to recognize patterns of PD signals obtained from the magnetic sensor. The technique consists of wavelet transformation to de-noise the signals, statistical analysis to extract features and multi-layer perceptron back propagation (MLP BP) neural network to classify different types of PD signals. The result is elaborated in this paper. � 2011 IEEE. Final 2023-12-29T07:47:43Z 2023-12-29T07:47:43Z 2011 Conference paper 10.1109/ICOS.2011.6079231 2-s2.0-83155163787 https://www.scopus.com/inward/record.uri?eid=2-s2.0-83155163787&doi=10.1109%2fICOS.2011.6079231&partnerID=40&md5=e01ef340f09ca6cd28b985f73635f2bf https://irepository.uniten.edu.my/handle/123456789/30427 6079231 243 246 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	neural network partial discharge pattern recognition statistical method time-resolved signals wavelet de-noising Backpropagation Feature extraction Magnetic sensors Partial discharges Pattern recognition Pattern recognition systems Statistical methods De-Noise Feature extraction methods Multi layer perceptron Partial discharge signal Time-resolved Wavelet denoising Wavelet transformations XLPE cables Neural networks
spellingShingle	neural network partial discharge pattern recognition statistical method time-resolved signals wavelet de-noising Backpropagation Feature extraction Magnetic sensors Partial discharges Pattern recognition Pattern recognition systems Statistical methods De-Noise Feature extraction methods Multi layer perceptron Partial discharge signal Time-resolved Wavelet denoising Wavelet transformations XLPE cables Neural networks Tho N.T.N. Chakrabarty C.K. Siah Y.K. Ghani A.B.Abd. Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
description	Magnetic sensor is a relatively new method to collect time-resolved partial discharge (PD) signals in XLPE cables. This paper proposes a simple yet effective method to recognize patterns of PD signals obtained from the magnetic sensor. The technique consists of wavelet transformation to de-noise the signals, statistical analysis to extract features and multi-layer perceptron back propagation (MLP BP) neural network to classify different types of PD signals. The result is elaborated in this paper. � 2011 IEEE.
author2	54584502600
author_facet	54584502600 Tho N.T.N. Chakrabarty C.K. Siah Y.K. Ghani A.B.Abd.
format	Conference paper
author	Tho N.T.N. Chakrabarty C.K. Siah Y.K. Ghani A.B.Abd.
author_sort	Tho N.T.N.
title	Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
title_short	Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
title_full	Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
title_fullStr	Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
title_full_unstemmed	Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
title_sort	feature extraction method and neural network pattern recognition on time-resolved partial discharge signals
publishDate	2023
_version_	1806423384714641408
score	13.222552

Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

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