Improvement of algorithm to reduce training time of back-propagation neural network for transformer interturn fault location

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dc.contributor.authorNgaopitakkul, A.
dc.contributor.authorPothisarn, C.
dc.contributor.authorKlomjit, J.
dc.contributor.authorBunjongjit, S.
dc.contributor.authorSuechoey, B.
dc.contributor.authorSuttisinthong, N.
dc.date.accessioned2026-08-06T10:05:01Z
dc.date.available2026-08-06T10:05:01Z
dc.date.issued2012-10-29
dc.description.abstractThis paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for location of interturn faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented by MATLAB. In addition, the choice of initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. © 2012 IEEE.
dc.identifier.citationInternational Conference on Wavelet Analysis and Pattern Recognition, 131-136, 2012
dc.identifier.doi10.1109/ICWAPR.2012.6294767
dc.identifier.issn21585695
dc.identifier.other2-s2.0-84867803137
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4412
dc.sourceInternational Conference on Wavelet Analysis and Pattern Recognition
dc.subjectInternal Winding Fault
dc.subjectNeural Network
dc.subjectTransformer
dc.subjectWavelet Transform
dc.titleImprovement of algorithm to reduce training time of back-propagation neural network for transformer interturn fault location
dc.typeConference Paper

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