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.
Format: Conference Paper
Language:en_US
Published: 2017
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spelling my.uniten.dspace-57152017-12-14T08:10:14Z 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. 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. 2017-12-08T06:45:39Z 2017-12-08T06:45:39Z 2011 Conference Paper 10.1109/ICOS.2011.6079231 en_US 2011 IEEE Conference on Open Systems, ICOS 2011 2011, Article number 6079231, Pages 243-246
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/
language en_US
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.
format Conference Paper
author Tho, N.T.N.
Chakrabarty, C.K.
Siah, Y.K.
Ghani, A.B.Abd.
spellingShingle 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
author_facet 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 2017
_version_ 1644493756614836224
score 13.211869