Semiconductor Sensor Array-Based Fault Diagnosis for High-Voltage Cable Insulation Erosion

This study investigates a fault diagnosis method for high-voltage cable insulation layer erosion, proposing a semiconductor sensor array-based fault identification technique combined with deep learning algorithms for accurate fault recognition. By simulating various fault conditions and collecting gas response data, the impact of each fault on gas response characteristics was analyzed. The experimental results indicate significant differences in the gas response data for severe moisture, radial current concentration, and suspended discharge faults, with distinct variations in time, amplitude, and stability for each fault. Additionally, PCA-Kmeans analysis showed that fault data can be effectively separated in the principal component space, providing a basis for fault classification. The study demonstrates that the semiconductor gas sensor array-based detection technology enables real-time monitoring and early warning of cable faults, which is of great significance for the safe operation of power systems.