High impedance fault detection technique based on Discrete Wavelet Transform and support vector machine in power distribution networks

Abstract

A High Impedance Fault (HIF) is a long standing complex type of a fault, because of its distinctive nature. The major concern with HIF is the potential risk it poses to human lives, because of its association with arcing. From the point of safety and reliability HIF is still a challenge for protection engineers. In this paper a HIF model is adopted and the combinations of wavelet transform and support vector machine is presented to detect a HIF. Discrete Wavelet Transform (DWT) is used as a feature extractor to extract useful information from the distorted HIF current signal. For classification purposes Support Vector Machine (SVM) is used to distinguish HIF from other events such as normal load, capacitor switching, and load switching. An Eskom network is studied and modelled in MATLAB/SIMULINK. The waveform results are fed into a DWT tool for feature extraction and the results from DWT are used to train the SVM for classification and ultimately detecting HIF.