Adaptive neuro-fuzzy inference system for monitoring the surface condition of polymeric insulators using harmonic content

Abstract

This study deals with the analysis of leakage current (LC) characteristics of polymeric insulator due to pollution under wet conditions and also adaptive neuro-fuzzy inference system (ANFIS)-based surface condition monitoring system is developed based on LC characteristics. In this work, laboratory-based pollution performance tests were carried out on 11 kV polymeric insulator under AC voltage at different pollution levels with sodium chloride as a contaminant. LC waveforms during the experimental studies were measured. Fast Fourier Transform was employed to understand the frequency domain characteristics of the LC signal. Correlation analysis of the harmonic contents of LC during the experimental study has been performed. ANFIS framework for surface condition monitoring has been proposed based on the conclusion made in correlation analysis. Reported results of this preliminary study on 11 kV polymeric insulator shows that the development of flashover due to pollution could be easily identified from the analysis of harmonic contents of LC signal, and ANFIS framework could be used to monitor the surface condition of the polymeric insulators.