Research on a Degradation Identification Method for GIS UHF Partial Discharge Sensors Based on S-Parameters

The ultra-high-frequency (UHF) detection method is highly accurate and has a fault localization function. At present, most gas-insulated switchgear (GIS) installations are equipped with online UHF monitoring devices to detect partial discharges. In order to ensure the accuracy of the detection results, UHF sensors need to be verified regularly. UHF sensors used for online monitoring are usually installed at the handhole of the GIS and cannot be removed. Measuring the laboratory verification indexes (e.g., equivalent height, dynamic range, etc.) of the sensors directly is very difficult. However, it is easier to measure S₁₁ of the sensor for verification and S₂₁ between it and the neighboring sensors by injecting power signals. Accordingly, this paper proposes a degradation identification method for GIS UHF sensors using a cross-comparison of S-parameters. When sensor sensitivity decreases, S₁₁ increases while S₂₁ decreases, both serving as effective indicators of performance degradation. In this study, the equivalent S-parameter network and the variation mechanisms of S₁₁ and S₂₁ during sensor verification were first analyzed. Normal and typically degraded sensor models were then constructed and coupled in different GIS structures for electromagnetic simulation. The simulation and on-site verification results show that S₁₁ is mainly affected by the sensor's intrinsic performance and installation conditions at the inspection port, whereas S₂₁ is predominantly influenced by sensor performance and the propagation characteristics of the GIS structure. Through cross-comparison of S₁₁ and S₂₁ at corresponding positions across three phases, sensor aging or failure can be effectively identified, enabling rapid on-site verification without removing the sensors. The proposed method was successfully validated on actual GIS equipment at the China Southern Power Grid Research Institute. It exhibits high accuracy, efficiency, and strong engineering applicability, enabling the early detection of degraded sensors and providing valuable support for condition assessment and maintenance decision-making in GIS online monitoring systems.