Discrete wavelet transform and support vector machines algorithm for classification of fault types on transmission line

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dc.contributor.authorKunadumrongrath, K.
dc.contributor.authorNgaopitakkul, A.
dc.date.accessioned2026-08-06T10:03:49Z
dc.date.available2026-08-06T10:03:49Z
dc.date.issued2012-01-01
dc.description.abstractThis paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify the fault types on transmission systems. The DWT is used to detect the high frequency components from fault signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detects fault are used as an input for the SVM. Various cases studies based on Thailand electricity transmission systems have been investigated so that the algorithm can be implemented. SVM is also compared with the comparison of the coefficients DWT technique as well as back-propagation neural network algorithm. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems.
dc.identifier.citationLecture Notes in Engineering and Computer Science, 2196, 1042-1046, 2012
dc.identifier.issn20780958
dc.identifier.other2-s2.0-84867458201
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4066
dc.sourceLecture Notes in Engineering and Computer Science
dc.subjectFault Classification
dc.subjectSupport Vector Machines
dc.subjectTransmission Line
dc.subjectWavelet Transform
dc.titleDiscrete wavelet transform and support vector machines algorithm for classification of fault types on transmission line
dc.typeConference Paper

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