Framework for XLPE Insulation using Space Charge Electrical Treeing and Partial Discharge Analysis

Cross-linked polyethylene (XLPE) insulation is widely used in high-voltage power cables due to its excellent electrical, thermal, and mechanical properties. However, its long-term reliability is significantly affected by degradation phenomena such as space charge accumulation, electrical treeing, and partial discharges (PD). These mechanisms are strongly nonlinear, multi-physics in nature, and evolve under varying electrical, thermal, and environmental stresses. Traditional diagnostic techniques often rely on handcrafted features and simplified physical interpretations, which may not fully capture the complexity of degradation patterns. In this context, deep learning frameworks have emerged as powerful tools for enhancing the diagnosis, classification, and prediction of insulation aging processes in XLPE systems. This abstract presents an overview of deep learningbased approaches for analyzing space charge behavior, treeing progression, and partial discharge activity in high-voltage insulation diagnostics. Space charge phenomena in XLPE significantly distort local electric fields,accelerating aging and breakdown processes. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been effectively applied to space charge measurement data obtained from techniques such as pulsed electroacoustic (PEA) systems. These models enable automated feature extraction from spatial-temporal charge distributions, allowing for improved classification of charge accumulation patterns and enhanced prediction of field distortion behavior. Furthermore, hybridarchitectures combining CNNs with long short-term memory (LSTM) networks have demonstrated superior performance in capturing both spatial profiles and temporal evolution of space charge dynamics under varying voltage stress conditions. Electrical treeing, a pre-breakdown phenomenon characterized by branched conductive channel formation, is another critical degradation mechanism in XLPE insulation. Deep learning frameworks utilizing image-based analysis have shown strong capability in identifying tree morphology and growth stages from microscopic and ultrasonic imaging data. Generative models and segmentation networks, such as U-Net architectures, are particularly effective in isolating tree structures and quantifying their propagation rates. These methods enable early-stage detection of insulation failure, offering predictive maintenance capabilities that surpass conventional threshold-based diagnostic approaches. Additionally, transfer learning techniques facilitate model adaptation across different insulation materials and voltage stress conditions, improving generalization performance in practical applications. Partial discharge analysis, a widely used non-invasive diagnostic method, benefits significantly from deep learning-based signal processing techniques. Overall, the integration of deep learning into XLPE insulation diagnostics provides a comprehensive, data-driven approach for understanding degradation mechanisms and improving the reliability of high-voltage power cable systems