Detection of Transmission Line Insulator Flashover Based on Categorized Discharge Intensity Level of Ultravoilet Signal

Abstract

Surface discharges are precursors to flashovers. To pre-empt the occurrence of flashover incidents, utility companies need to regularly monitor the condition of line insulators. Recent studies have shown that monitoring of ultra-violet (UV) signals emitted by surface discharges of the insulators is a promising technique. In this study, set of contaminated and aged insulator was used. UV pulse signal of surface discharges activities on these insulators was recorded and analyzed. Experimental result showed that a strong correlation exists between the discharge intensity levels under varying contamination levels and degree of ageing. As the contamination level increased, the discharge levels of the insulator samples intensified, resulting in the increase of total harmonic distortion (THD). THD of the UV signals have been employed by using MATLAB simulation to develop a technique based on artificial neural network (ANN) to classify the flashover prediction based on the discharge intensity levels of the insulator samples. The results of the ANN simulation showed 87% accuracy in the performance index. This study illustrates that UV pulse detection method can be a potential tool to monitor insulator surface conditions during service.