High Impedance Fault Classification and Localization Method for Power Distribution Network

Abstract

This paper presents a technique for high impedance fault diagnosis in a power distribution network. A segment of a 22 kV Eskom real power system is modelled in Power World Software. Various cases including High Impedance Faults (HIFs), normal condition, load switching and capacitor switching are subsequently investigated through the study of the model. Discrete Wavelet Transform (DWT) is used as a feature extraction technique. The extracted features are subsequently fed into an Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) Schemes in order to effectively diagnose HIFs. ANN is used for fault classification and detection from other power system conditions. Furthermore, the GPR scheme is used to estimate the location of the fault. Thus a hybrid technique which comprises of DWT-ANN-GPR is proposed for HIF diagnosis. The results obtained shows that the scheme is fairly accurate and has minimum estimation error for fault location.