State Monitoring and Fault Detection of GIS Isolation Disconnector Based on Spearman Rank Correlation Coefficient

This study develops an academic approach for monitoring Gas Insulated Switchgear (GIS) isolation switches using the Spearman rank correlation coefficient. GIS equipment is critical in power systems but is susceptible to various operational factors. Conventional methods are hindered by sensor failures and data inaccuracies, necessitating automated solutions. Our method, based on Spearman rank correlation analysis of motor power curves, effectively detects abnormal GIS equipment operation. In experiments, we collected motor voltage and current data under normal and simulated fault conditions, successfully deriving power curves. Results demonstrate the method's efficacy for GIS equipment monitoring and fault detection, enhancing power system reliability. This research offers innovative insights to improve power equipment monitoring, potentially enhancing maintainability and availability in power systems.