Prediction of dielectric parameters of an aged mv cable: A comparison of curve fitting, decision tree and artificial neural network methods

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dc.authoridUydur, Cihat Cagdas/0000-0002-0908-2722
dc.authoridKUMRU, Celal Fadil/0000-0003-4248-6355
dc.authoridARIKAN, OKTAY/0000-0002-3304-3766
dc.authorwosidUydur, Cihat Cagdas/AFR-1415-2022
dc.authorwosidKUMRU, Celal Fadil/KHY-5776-2024
dc.authorwosidARIKAN, OKTAY/AAZ-9133-2020
dc.contributor.authorArikan, Oktay
dc.contributor.authorUydur, Cihat Cagdas
dc.contributor.authorKumru, Celal Fadil
dc.date.accessioned2024-06-12T10:59:40Z
dc.date.available2024-06-12T10:59:40Z
dc.date.issued2022
dc.departmentTrakya Üniversitesien_US
dc.description.abstractIn most dielectric diagnosis studies on medium voltage cables, aging methods, which requires quite long measurement durations, are preferred and dielectric performance of cable is generally measured at the end of the test period. In addition, changes that occur during the aging cycle should be investigated. Predicting the future performance of a cable by using dielectric parameters measured during the aging cycle is quite important in terms of estimating possible failures. In this regard, the effectiveness of interpolation and extrapolation methods commonly used in the literature should be investigated in order to shorten aging durations and to predict future insulation performance. In this study, 12/20.8 kV rated voltage and XLPE insulated medium voltage cable was aged with 60 kV (5 center dot U-0) overvoltage for 80 cycles of 15 min. After each aging cycle, dielectric parameters (dissipation factor (tan delta), dielectric losses (P-k) and capacitance (C) were measured at rated voltage and mains frequency. Following the measurements, interpolation and extrapolation analyses were performed using artificial neural network (ANN), decision tree (DT) and curve fitting (CF) methods. As a result, interpolation and extrapolation performances of methods are comparatively discussed and introduced. It has been determined that ANN algorithm is the most successful method.en_US
dc.description.sponsorshipScientific Research Project Coordinator of Yildiz Technical University [FDK2020-3897]; Yildiz Technical Universityen_US
dc.description.sponsorshipScientific Research Project Coordinator of Yildiz Technical University supported this research study with the project number FDK2020-3897. The authors would like to thank the Yildiz Technical University for financial support.en_US
dc.identifier.doi10.1016/j.epsr.2022.107892
dc.identifier.issn0378-7796
dc.identifier.issn1873-2046
dc.identifier.scopus2-s2.0-85125449280en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.epsr.2022.107892
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20535
dc.identifier.volume208en_US
dc.identifier.wosWOS:000865738400009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Saen_US
dc.relation.ispartofElectric Power Systems Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDissipation Factoren_US
dc.subjectXLPE Cableen_US
dc.subjectOvervoltageen_US
dc.subjectAgingen_US
dc.subjectInterpolationen_US
dc.subjectExtrapolationen_US
dc.subjectXlpe Cableen_US
dc.subjectMedium-Voltageen_US
dc.subjectInsulationen_US
dc.titlePrediction of dielectric parameters of an aged mv cable: A comparison of curve fitting, decision tree and artificial neural network methodsen_US
dc.typeArticleen_US

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