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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.
Inspec keywords: fuzzy neural nets; computerised monitoring; fuzzy reasoning; fast Fourier transforms; leakage currents; power engineering computing; frequency-domain analysis; insulator contamination; condition monitoring; polymer insulators; harmonic analysis
Other keywords:
Subjects: Integral transforms; Mathematical analysis; Knowledge engineering techniques; Power line supports, insulators and connectors; Mathematical analysis; Power engineering computing; Dielectric materials and properties; Computerised instrumentation; Neural computing techniques; Integral transforms