An Approach to Detection of High Impedance Fault Using Discrete Wavelet Transform and Artificial Neural Networks

High impedance faults (HIF) are faults that are difficult to detect by conventional protection relays. In this paper, a new HIF model is introduced and a novel methodology is presented to detect HIF by means of discrete wavelet transform (DWT) and artificial neural network (ANN). The distorted waveforms (HIF, load switching, line switching, capacitor switching and non-linear loads that behave similar to HIF current) are generated using PSCAD/EMTDC, captured with a sampling rate of 20 kHz and de-noised using DWT to obtain signals with higher signal-to-noise ratio. DWT is used to decompose the distorted signal and to extract its useful information. Appropriate feature vectors are created and applied in training the ANN. The effectiveness of the proposed method was tested using a wide spectrum of disturbances. Simulations are carried out to confirm the suitability and capability of the proposed method in HIF detection.