Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line

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dc.contributor.authorPothisarn, C.
dc.contributor.authorNgaopitakkul, A.
dc.date.accessioned2026-08-06T09:59:27Z
dc.date.available2026-08-06T09:59:27Z
dc.date.issued2009-12-16
dc.description.abstractThis paper proposes a technique using Discrete Wavelet Transform (DWT) and Back-Propagation Neural Network (BPNN) to identify the fault types on single circuit transmission lines. The ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results.
dc.identifier.citationTransmission and Distribution Conference and Exposition Asia and Pacific T and D Asia 2009, 2009
dc.identifier.doi10.1109/TD-ASIA.2009.5356921
dc.identifier.other2-s2.0-76249130087
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2829
dc.sourceTransmission and Distribution Conference and Exposition Asia and Pacific T and D Asia 2009
dc.subjectATP/EMTP
dc.subjectDiscrete wavelet transform
dc.subjectFault classification
dc.subjectNeural network
dc.subjectTransmission line
dc.titleDiscrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line
dc.typeConference Paper

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