Support Vector Machine Based Method for High Impedance Fault Diagnosis in Power Distribution Networks

Abstract

The detection of high impedance faults (HIFs) on a power distribution system has been a subject of concern for many decades. This poses a very unique challenge to the protection engineers, as it seems to be invisible to be detected by conventional protection schemes. The major concern about HIFs is that they pose a safety risk, as these faults are associated with arcing which may be dangerous for the surroundings. In this work, we propose a technique, which uses feature extraction, classification and a locating algorithm. Discrete wavelet transform (DWT) is used to extract meaningful information, support vector machine (SVM) is used as a classifier and a support vector regression (SVR) scheme is used as a fault location estimator. The technique is tested on a network of a power utility.