Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/8910
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dc.contributor.authorTho, N.T.N.
dc.contributor.authorChakrabarty, C.K.
dc.contributor.authorSiah, Y.K.
dc.contributor.authorGhani, A.B.Abd.
dc.date.accessioned2018-02-21T04:42:09Z-
dc.date.available2018-02-21T04:42:09Z-
dc.date.issued2011
dc.identifier.urihttp://dspace.uniten.edu.my/jspui/handle/123456789/8910-
dc.description.abstractMagnetic 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.
dc.titleFeature extraction method and neural network pattern recognition on time-resolved partial discharge signals
item.fulltextNo Fulltext-
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