High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion

High-impedance faults (HIFs) are faults which are difficult to detect by overcurrent protection relays. Various pattern recognition techniques have been suggested, including the use of wavelet transform . However this method cannot indicate the physical properties of output coefficients using the wavelet transform. We propose to use the Discrete Wavelet Transform (DWT) as well as frequency range and rms conversion to apply a pattern recognition based detection algorithm for electric distribution high impedance fault detection. The aim is to recognize the converted rms voltage and current values caused by arcs usually associated with HIF. The analysis using discrete wavelet transform (DWT) with the conversion yields measurement voltages and currents which are fed to a classifier for pattern recognition. The classifier is based on the algorithm using nearest neighbor rule approach. It is proposed that this method can function as a decision support software package for HIF identification which could be installed in an alarm system.