An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line

Simple item page
dc.contributor.authorNgaopitakkul, A.
dc.contributor.authorBunjongjit, S.
dc.date.accessioned2026-08-06T10:07:08Z
dc.date.available2026-08-06T10:07:08Z
dc.date.issued2013-09-01
dc.description.abstractThis article proposes an application of the discrete wavelet transform (DWT) and back-propagation neural networks (BPNN) for fault diagnosis on single-circuit transmission line. ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is used to decompose the high-frequency component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and faulty phases are detailed. The DWT is employed in extracting the high frequency component contained in the fault currents, and the coefficients of the first scale from the DWT that can detect fault are investigated, and the decision algorithm is constructed based on the BPNN. The results show that the proposed technique provides satisfactory results. © 2013 Taylor & Francis Group, LLC.
dc.identifier.citationInternational Journal of Systems Science, 44(9), 1745-1761, 2013
dc.identifier.doi10.1080/00207721.2012.670290
dc.identifier.issn00207721
dc.identifier.other2-s2.0-84878741042
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5005
dc.sourceInternational Journal of Systems Science
dc.subjectback-propagation neural networks
dc.subjectdiscrete wavelet transform
dc.subjectfault diagnosis
dc.subjectfault location
dc.subjecttransmission line
dc.titleAn application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line
dc.typeArticle

Files

Collections

Research