A Review and Progress of Insulation Fault Diagnosis for Cable Using Partial Discharge Approach

Abstract

The cable is an essential pivot of electric energy transmission, and its operational condition influences the safety and stability of both the power system (PS) and traction power supply system (TPSS). The prompt detection of cable insulation conditions by partial discharge (PD) monitoring is crucial for mitigating potential damage to the cables. To begin with, this article provides a comprehensive overview of PD detection approaches for cables by introducing the mechanism of PD in cables. The detection approaches are categorized into electrical detection techniques and nonelectrical detection techniques. Afterward, to accurately assess the insulation condition of cables using the PD detection method, this article summarizes the insulation defect diagnosis approaches for cables. In particular, the insulation defect diagnosis approaches primarily comprise extraction and optimization for features, traditional machine-learning-based fault diagnosis approaches, and deep-learning-based fault diagnosis approaches. To conclude, this article summarizes the challenges and future research directions of cable fault diagnosis. Accurate cable fault diagnosis is fundamental to maintaining the reliable operation of cables, ensuring an uninterrupted power supply to both PS and TPSS, and enhancing the responsiveness to equipment failures.