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dc.contributor.authorKantar, Emre
dc.contributor.authorCascallo, Jaume M.
dc.contributor.authorAakre, Torstein Grav
dc.contributor.authorThomsen, Nina Marie
dc.contributor.authorEberg, Espen
dc.description.abstractPartial discharges (PD) in the high voltage insulation systems are both a symptom and cause of terminal and impending failures. The use of data-driven methods based on PD measurements will enable predictive strategies to replace traditional maintenance strategies. This paper employs machine learningbased classification models to identify and characterize PD signals originating from lab-made artificial defects in epoxy-mica material samples. Three different PD sources were studied: surface discharges in air, corona discharges, and discharges caused by internal cavities/delaminations. To generate high-quality datasets for the training, validation, and testing of classification models, Phase-Resolved PD (PRPD) data for each test object was obtained at room temperature under 50 Hz AC excitation at 10 % above the PD inception voltage (PDIV) of each sample. Relevant statistical and deterministic features were extracted for each observation and were labeled based on the defect type (supervised learning). Finally, the trained and validated ML models were used to identify PD sources in the service-aged stator winding insulation. Support vector machines (SVM), ensemble, and k-nearest neighbor (kNN) algorithms achieved significantly high accuracy (≥ 95 %) of defect identification.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleUse of Data-Driven Approaches for Defect Classification in Stator Winding Insulationen_US
dc.title.alternativeUse of Data-Driven Approaches for Defect Classification in Stator Winding Insulationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holderThe Authorsen_US
dc.source.journalProceedings of the Nordic Insulation Symposiumen_US
dc.relation.projectNorges forskningsråd: 257588en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal