Artificial Neural Networks Used for ZnO Arresters Diagnosis

Abstract

Lightning arresters provide protection for transmission lines and power equipments. During the occurrence of lightning or switching surges, a voltage level above the equipment insulation level can be reached. An arrester, properly working, is able to limit this voltage level avoiding damages to the protected equipment. A defective arrester may not be able to provide the proper protection level in substations, exposing the equipment and personnel to damage. Monitoring of surge arresters is usually conducted by means of current measurement or thermal images acquisition. But, among power companies, there is no model procedure for the monitoring conduction and analysis of the obtained results. Besides that, when some abnormality is detected, the arrester is replaced by a new one and no further study is conducted to evaluate what kind of problem happened to it. This work proposes a method for analysis of ZnO arresters by the study of failures and usage of artificial neural networks-ANN. The ANN is able to analyze the thermal profile, detect and classify patterns that could be undetected by a visual analysis.