Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings

Simple item page
dc.creatorDhlamini, Sizwe M.
dc.creatorMarwala, Tshilidzi
dc.creatorMajozi, Thokozani
dc.date2007-05-16
dc.date.accessioned2026-07-07T08:01:49Z
dc.date.available2026-07-07T08:01:49Z
dc.descriptionThe work proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The diagnosis uses dissolved gas analysis (DGA) data from bushings based on IEC60599 and IEEE C57-104 criteria for oil impregnated paper (OIP) bushings. FST and neural networks are compared in terms of accuracy and computational efficiency. Both FST and NN simulations were able to diagnose the bushings condition with 10% error. By using fuzzy theory, the maintenance department can classify bushings and know the extent of degradation in the component.
dc.description7 pages
dc.identifierhttps://arxiv.org/abs/0705.2305
dc.identifierhttp://arxiv.org/abs/0705.2305
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/129036
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.titleFuzzy and Multilayer Perceptron for Evaluation of HV Bushings
dc.typetext

Files

Collections

arXiv