Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4673
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dc.contributor.authorSahoo, Raseswar-
dc.contributor.authorPanigrahy, Satyajit-
dc.contributor.authorKarmakar, Subrata-
dc.date.accessioned2024-09-10T07:19:17Z-
dc.date.available2024-09-10T07:19:17Z-
dc.date.issued2024-08-
dc.identifier.citationIEEE International Conference on High Voltage Engineering and Application (ICHVE), Berlin, Germany, 18-22 August 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4673-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractElectrical aging is one of the primary contributors to the insulation degradation of a power cable, along with the initiation and progression of partial discharge. Henceforth, the study of partial discharge is an important indicator to evaluate the degree of degradation of the insulation. In this work, a XLPE insulation sample was electrically aged for a total of 80 hours at 28 kV, and PD signals were collected at two distinct instants, i.e., after 40 and 80 hours. From the collected signals, a total of nine statistical features were extracted to train different machine learning techniques like Logistic Regression, K-Nearest Neighbors, Decision Tree, and XgBoost for the classification of the moderately and highly aged condition of the XLPE cable insula tion. Furthermore, FTIR was used to investigate the insulation’s material characterization. The experimental findings indicated that the XgBoost method showed effectiveness as compared to others, with an accuracy of 93.33%. FTIR results indicated an increase in the oxidative degradation with the progression of aging.en_US
dc.subjectPartial Discharge (PD)en_US
dc.subjectAgingen_US
dc.subjectMachine Learning (ML)en_US
dc.subjectFTIRen_US
dc.titleAging State Recognition of a Crosslinked Polyethylene Power Cable Insulation using Machine Learning and Fourier Transform Infrared Spectroscopyen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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