Investigation of XLPE Cable Insulation Using Electrical, Thermal and Mechanical Properties, and Aging Level Adopting Machine Learning Techniques

Abstract

Hydrothermal and chemical aging tests on a 230 kV cross-linked polyethylene (XLPE) insulation cable were carried out in the present study to evaluate the degradation and aging levels qualitatively. The samples were subjected to water aging at a temperature of 80 °C, and in an aqueous ionic solution of CuSO4 at room temperature. The diffusion coefficient results indicated that the ion migration was not at the same rate in the aging conditions. The diffusion coefficient–D–of the sample immersed in an aqueous CuSO4 solution was lower than the hydrothermally aged specimens. The hydrophobicity of aged specimens decreased considerably compared to unaged samples. The distribution of trapped charges was quantitatively characterized. The presence of shallow trap energy states were observed in unaged XLPE, whereas the deep trap sites were noticed in aged specimens. In addition, the charge trap characteristics were different for positive and negative charge deposition. Various material characterization techniques, viz. dynamic mechanical analysis (DMA), tensile, contact angle, and LIBS, were further employed on the aged and virgin specimens. The tensile behavior of the hydrothermally aged specimen was degraded due to the oxidised regions, which had formed a weak spot against the mechanical stress. Reduced glass transition temperature and increased loss tangent measurements were noticed for aged specimens over their unaged counterparts. Machine learning techniques, such as the principal component analysis (PCA) and the artificial neural network (ANN) analysis, were performed on LIBS spectral data of the samples to classify the aging mechanisms qualitatively.

Keywords

• aging
• diffusion coefficient
• trap depth
• LIBS
• XLPE
• machine learning