Wavelet Transform Based Algorithm for High- Impedance Faults Detection in Distribution Feeders

This paper presents a wavelet transform based technique for high-impedance faults detection in power distribution feeders. In this study, accurate electrical models for a HIF and capacitor switching event on a power network are developed and simulated using MATLAB. The analysis of the resulted fault signals, using the Discrete Wavelet Transform (DWT) yields single-phase current and voltage in the low frequency range, which are fed to a classifier for pattern recognition. The classifier algorithm in this paper is based on a moving window approach whereby the one-cycle window of the DWT output is moved continuously by one sample. The algorithm when tested with data obtained from various computer simulations carried out in this study, produced impressive results in HIF detection and discrimination. The major contribution of this work is that it is able to determine the magnitude of the fault current, which many earlier researchers did not consider in their works.