Comprehensive Analysis of PD Measurement, Condition Assessment, and Electrical Tree Growth Characteristics in Polymeric Cable Insulation

Abstract

The presence of partial discharge (PD) activities deteriorates the high voltage cross-linked polyethylene (XLPE) cable, due to void, crack, and contamination of impurities. For this, the detection and classification of PD signal in XLPE cable is very important to find out the root cause of insulation failure. The existence of void amplifies the electric field stress caused by variations in the dielectric constant between air and the insulation that is all around it. PD activities on such XLPE insulation under high voltage stress are responsible for the growth of electrical- trees. The persistence of these discharges replicates several carbonyl channels which are in the form of an electrical tree. The growth rate of treeing phenomena implies a sudden and complete breakdown of in-service cable. Therefore, the detection of the various stages of electrical tree growths is essential for the safety and ensuring the reliable operation of the XLPE cable over a longer period. Deep learning techniques have given opportunities for automated feature extraction and classification of the severity level of discharge activity during tree growth. In this work, the XLPE sample specimens are aged at different applied AC voltages, and the respective PD signals are continuously saved for every cycle. PD signals are used for different stages of pattern classification during the growth of an electrical tree with the help of deep learning architectures. Also, this study presents a novel framework for identifying the evolution of electrical trees using image processing techniques. For this study, microscopic images of electrical trees are captured in the HV laboratory by variation of operating voltage and environment temperature. The growth characteristics are employed along with the fractal dimension to differentiate between different growth stages that occur after the initiation of tree development, including fast growth, stagnated growth, and the breakdown stage. To accomplish the automated identification of the severity level of XLPE in the form of tree growth is achieved with the help of ViT and transfer learning architecture. Along with this, the impact of thermal aging in terms of the electrical and physicochemical characteristics of XLPE insulation is emphasized. In order to accelerate the aging process, 33 kV high voltage XLPE cable samples are heated for 120 and 240 hours at 150°C, compared to an unaged sample. Due to thermal aging micro-voids, cracks are formed and the crystallization of polymeric material deteriorates, which affects the electrical tree growth characteristics. Also, during the aging formation of carbonyl index, molecular change, degree of crystallinity, enthalpy of fusion, and nature of hydrophobicity is observed by Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), X-ray diffraction (XRD), Differential scanning calorimetry (DSC), and Contact angle measurement respectively. The results show that the microstructure of the unaged sample is more resistant to thermal and oxidative stress than that of the aged sample. The molecular structure of the sample breaks down due to the thermo-oxidation reaction, and the crystal area is damaged as a result of long-term use, which decreases insulation performance.