A Fault Diagnosis Method for Surge Arresters Based on Distributed In Situ Measurement Technology

Abstract

Metal oxide surge arresters (MOAs) are core overvoltage protection devices in power systems. However, conventional single-parameter MOA fault diagnostic methods exhibit low accuracy and limited anti-interference capability in complex operating conditions. In this article, a distributed in situ measurement technique and a lightweight analytical criterion for MOA fault diagnosis are proposed. First, a measurement system is designed based on a distributed parameter equivalent circuit model, which deploys eight embedded sensors within the valve column along the axial direction. This architecture enables synchronous acquisition of the longitudinal leakage current vector and temperature profile. Second, experiments are performed under three typical conditions of normal operation, surface fouling, and valve disk failure to reveal the distribution characteristics of temperature and leakage current. Third, an analytical criterion is proposed, which integrates multiple feature parameters, including leakage current inhomogeneity coefficient, full current, and maximum temperature rise. Last, the K-means clustering method is adopted to determine thresholds for three typical operating conditions. The experimental results show that the proposed criterion achieves an accuracy of 96.67 %, outperforming single-feature and traditional machine learning methods. The research provides a new approach for MOA fault diagnosis.