1. DSpaceCRIS@UNITEN
  2. College of Engineering
  3. COE Scholarly Publication
Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/5715
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dc.contributor.authorTho, N.T.N.en_US
dc.contributor.authorChakrabarty, C.K.en_US
dc.contributor.authorSiah, Y.K.en_US
dc.contributor.authorGhani, A.B.Abd.en_US
dc.date.accessioned2017-12-08T06:45:39Z-
dc.date.available2017-12-08T06:45:39Z-
dc.date.issued2011-
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.en_US
dc.language.isoen_USen_US
dc.relation.ispartof2011 IEEE Conference on Open Systems, ICOS 2011 2011, Article number 6079231, Pages 243-246en_US
dc.titleFeature extraction method and neural network pattern recognition on time-resolved partial discharge signalsen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1109/ICOS.2011.6079231-
item.grantfulltextnone-
item.fulltextNo Fulltext-
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