Detecting winding to ground fault locations in power transformers using back-propagation neural networks

Simple item page
dc.contributor.authorNgaopitakkul, A.
dc.contributor.authorKunakorn, A.
dc.date.accessioned2026-08-06T09:54:25Z
dc.date.available2026-08-06T09:54:25Z
dc.date.issued2006-12-01
dc.description.abstractThis paper presents an algorithm based on a combination of discrete wavelet transforms and neural networks for detecting locations of winding to ground faults in a two-winding three-phase transformer. The fault conditions of the transformer are simulated using ATP/EMTP in order to obtain fault current signals used as an input for a training process of a back-propagation neural network. The training process and fault diagnosis decision algorithm are implemented using toolboxes on MATLAB/Simulink. Various cases studies based on Thailand electricity transmission and distribution systems are performed to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a fault diagnosis process for a transformer manufacturer.
dc.identifier.citationIet Conference Publications, 2006
dc.identifier.doi10.1049/cp:20062112
dc.identifier.other2-s2.0-70350241231
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/1373
dc.sourceIet Conference Publications
dc.subjectDiscrete Wavelet Transforms
dc.subjectInternal faults
dc.subjectTransformer windings
dc.titleDetecting winding to ground fault locations in power transformers using back-propagation neural networks
dc.typeConference Paper

Files

Collections

Research