Power system transformerboard degradation detection using probabilistic neural network

dc.authorid0000-0002-8113-0514en_US
dc.contributor.authorCekli S.
dc.contributor.authorUzunoglu C.P.
dc.contributor.authorUgur M.
dc.date.accessioned2024-07-12T21:51:36Z
dc.date.available2024-07-12T21:51:36Z
dc.date.issued2012en_US
dc.departmentMaltepe Üniversitesien_US
dc.description.abstractThe insulation condition monitoring of a power transformer has an important role for insulating materials which are subjected to extensive breakdown stress. In this study, a test setup has been constructed in order to simulate real world breakdown characteristics of transformerboards which are widely used as the insulating material. During the service life transformerboards may display undesired surface discharge damage due to increased rated voltages, which reduces the lifetime of transformerboards. The probabilistic neural network is used to detect the surface degradation of a transformerboard by analyzing electrical and ultrasound discharge data obtained from the test setup. The principle component analysis is employed to eliminate the messy matrix and vector calculations of the probabilistic neural network operations. Results of the classification procedure are given. © 2005 - 2012 JATIT & LLS.en_US
dc.identifier.endpage7en_US
dc.identifier.issn1992-8645
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-84867525557en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/8287
dc.identifier.volume42en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherAsian Research Publishing Network (ARPN)en_US
dc.relation.ispartofJournal of Theoretical and Applied Information Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY02079
dc.subjectPrinciple component analysisen_US
dc.subjectProbabilistic neural networken_US
dc.subjectTransformerboarden_US
dc.titlePower system transformerboard degradation detection using probabilistic neural networken_US
dc.typeArticle
dspace.entity.typePublication

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