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Artificial neural network modelling of partial discharge parameters for transformer oil diagnosis

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dc.citedby13
dc.contributor.authorFoo J.S.T.en_US
dc.contributor.authorGhosh P.S.en_US
dc.contributor.authorid6603695984en_US
dc.contributor.authorid55427760300en_US
dc.date.accessioned2023-12-28T08:58:00Z
dc.date.available2023-12-28T08:58:00Z
dc.date.issued2002
dc.description.abstractOn-line partial discharge (PD) detection technique is gaining importance as a condition-monitoring test for transformers. Presently on-line condition-monitoring efforts of transformers are primarily directed towards chemical analysis of the oil. In this paper, a new approach has been proposed to interpret the condition of the transformer oil from PD results. An experiment was carried out on a simulated transformer tank to obtain PD data under three different oil conditions (e.g. clean oil, oil with solid contaminants and oil with high moisture content). Besides the oil condition, the loading of the transformer was also simulated by varying the temperature of the oil. The PD parameters obtained from the experiment are PD magnitude (q) and number of counts (n). The experimental results were then modelled using the Multi-layer Feedfoward Neural Network (NN) with Back Propagation technique. Once trained, the NN model (i.e. "oil condition = f(q, n)") is then capable of predicting the condition of the transformer oil, under any given set of q, n with a mean absolute error (MAE) less than 5%. The outcome of this research provides an alternative method of diagnosing the condition of the transformer oil without the need for conventional chemical analysis.en_US
dc.description.natureFinalen_US
dc.identifier.epage473
dc.identifier.scopus2-s2.0-0036440380
dc.identifier.spage470
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0036440380&partnerID=40&md5=388a12680eff678f6cb6061c783d0154
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29871
dc.pagecount3
dc.sourceScopus
dc.sourcetitleConference on Electrical Insulation and Dielectric Phenomena (CEIDP), Annual Report
dc.subjectComputer simulation
dc.subjectElectric breakdown
dc.subjectInsulating oil
dc.subjectNeural networks
dc.subjectMulti-layer feedfoward neural networks
dc.subjectPartial discharges
dc.titleArtificial neural network modelling of partial discharge parameters for transformer oil diagnosisen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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