High-impedance Faults Analysis in Distribution Networks Using an Adaptive Neuro Fuzzy Inference System

Abstract

This article presents a new approach for high-impedance fault analysis (detection, classification, and location) in distribution networks using the adaptive neuro fuzzy inference system. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed in this article. The results show that the proposed technique has very good performance in detecting, classifying, and locating high-impedance faults. The third harmonics, magnitude, and angle for the three-phase currents give superior results for fault detection as well as for fault location in high-impedance faults. The fundamental components magnitude and angle for the three-phase currents give superior results for the classification phase of high-impedance faults over other types of data inputs.