High Impedance Fault Detection Based on Wavelet Transform and Statistical Pattern Recognition

Abstract

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.