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.

Water-tree is a serious problem in the underground power cables due to degradation mechanism. Therefore, it is progressively significant to study the effect of water-tree by modeling vented trees at the outer and inner surface of the insulation. The existence of a vented water tree is the common fault in aged cable leading to cable deterioration. Hence, the precise model emphasizes water trees using the Finite element method (FEM) under different levels of growth in insulation. Diagnostic techniques are still needed to enable the estimation of the level of damage present within a cable. This paper reported about the power cable current scenario of diagnostics method for pulse voltage application on 33 kV cross-linked polyethylene (XLPE) insulated cable used as a simulation model. Therefore, the propagation of high-frequency pulse within the cable that undergoes reflections from water trees could be investigated and its velocity of propagation was explored. The water tree lumped parameters are simulated and calculated analytically at different levels of growth across the insulation. Consequently, the diagnostics method developed by the way of the proper approach to detect water-tree based on charge intensity in electrical power cables.

The reliability and efficiency of transformers are crucial for the stable operation of power distribution systems. This project focuses on the Condition Monitoring of Transformers Based on IoT Technology, enabling real-time monitoring of critical parameters such as temperature, oil level, voltage. By integrating sensors, Arduino Uno, and the NodeMCU ESP8266 WiFi module, the system continuously collects data and transmits it to a cloud-based platform for remote monitoring and analysis. The implementation of IoT technology enhances predictive maintenance, reducing unexpected failures and minimizing downtime. Additionally, data-driven insights help optimize transformer performance, extend its lifespan, and reduce maintenance costs. This approach ensures a more efficient, reliable, and cost-effective power distribution network.