Condition Assessment of Metal Oxide Surge Arrester Using Machine Learning Techniques

The development of the electric power grid has boosted the use of metal oxide surge arresters in electrical systems. These devices serve as protective measures and aid in insulation coordination. Monitoring the condition of surge arresters is crucial to ensure the reliability of the power system. Among various techniques, conditional monitoring based on leakage current signals, particularly resistive leakage current analysis is popular and effective. In this work, the resistive leakage current has been separated from the measured total leakage current signal and several features have been extracted to train the machine learning model for suitable identification of the contamination level of the surge arresters.