Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4673
Title: Aging State Recognition of a Crosslinked Polyethylene Power Cable Insulation using Machine Learning and Fourier Transform Infrared Spectroscopy
Authors: Sahoo, Raseswar
Panigrahy, Satyajit
Karmakar, Subrata
Keywords: Partial Discharge (PD)
Aging
Machine Learning (ML)
FTIR
Issue Date: Aug-2024
Citation: IEEE International Conference on High Voltage Engineering and Application (ICHVE), Berlin, Germany, 18-22 August 2024
Abstract: Electrical 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.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4673
Appears in Collections:Conference Papers

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