A method to enhance the predictive maintenance of ZnO arresters in energy systems

Abstract

Leakage current and temperature are common features of zinc oxide (ZnO) arrester degradation in power systems; however, for substation engineers, these key features often bring difficulties in grasping the operating conditions of ZnO arresters because of insufficient maintenance references. Therefore, in this study, the aim is to propose a systematic method to facilitate the realization of predictive maintenance of ZnO arresters in energy systems. The method begins with the feature extraction of ZnO arresters by using on-line resistive current monitoring and infrared radiation (IR) image inspection. A regression model based on the temperature difference, the total leakage current, and the resistive leakage current is then formulated to assist in the insulation diagnosis. This approach is useful to observe the insulation condition, thereby benefiting the predictive maintenance of ZnO arresters. In order to validate the effectiveness of the method, it has been applied to inspect several ZnO arresters installed in a 345kV substation in Taiwan. Results show that this proposed method performs more effectively than published techniques.