Discrimination between external short circuit and internal winding fault in power transformer using discrete wavelet transform and back-propagation neural network

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dc.contributor.authorJettanasen, C.
dc.contributor.authorKlomjit, J.
dc.contributor.authorBunjongjit, S.
dc.contributor.authorNgaopitakkul, A.
dc.contributor.authorSuechoey, B.
dc.contributor.authorSuttisinthong, N.
dc.contributor.authorSeewirote, B.
dc.date.accessioned2026-08-06T10:03:42Z
dc.date.available2026-08-06T10:03:42Z
dc.date.issued2012-01-01
dc.description.abstractThis paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for detecting and identifying internal winding fault of three-phase two-winding transformer. The maximum ratio obtained from division algorithm between coefficient from DWT of differential current and zero sequence for post-fault differential current waveforms is employed as an input for the training pattern in order to discriminate between internal fault and external short circuit. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique has good accuracy to detect fault and to identify its position in the considered system. © 2012 IEEE.
dc.identifier.citationProceedings International Conference on Machine Learning and Cybernetics, 3, 965-971, 2012
dc.identifier.doi10.1109/ICMLC.2012.6359484
dc.identifier.issn2160133X
dc.identifier.other2-s2.0-84871605097
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4042
dc.sourceProceedings International Conference on Machine Learning and Cybernetics
dc.subjectExternal short circuit
dc.subjectInternal winding fault
dc.subjectPower transformer
dc.subjectWavelet transform
dc.titleDiscrimination between external short circuit and internal winding fault in power transformer using discrete wavelet transform and back-propagation neural network
dc.typeConference Paper

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