Sensing Surface Contamination of Metal Oxide Surge Arrester Through Resistive Leakage Current Signal Analysis by Mathematical Morphology

This paper presents an advanced method of sensing surface contamination of polymeric housed Metal Oxide Surge Arrester through resistive leakage current signal analysis. Surface condition of polymeric housed MOSAs often gets contaminated due to accumulation of dust and other pollutant. Accumulated pollutants can degrade the condition of the arrester due to overheating which may lead to explosion. Therefore, reliability of power system may get affected due to failure of MOSAs. Resistive leakage current analysis of MOSA is one of the conventional method for sensing surface contamination of surge arrester. In this article, Mathematical Morphology operator has been introduced to extract various features from the resistive part of leakage current signals measured at different surface contamination level. Further, the extracted features have been trained through Gaussian Naïve Bayes (GNB) and surface contamination level of MOSA has been identified through this classifier. Result shows that proposed technique provides satisfactory outcomes regarding condition monitoring of MOSA at different surface contamination level which in turn enhances the reliability of system. The proposed technique is generic in nature and well suited for any other similar kind of applications.