Fuzzy based methodologies comparison for high-impedance fault diagnosis in radial distribution feeders

Abstract

This study presents a comparison of two developed intelligent systems that carries out, in an integrated manner, failure diagnosis on electric power distribution feeders. These procedures aim to identify and classify critical situations, as high-impedance faults, which can potentially damage the system components and cause power supply interruptions to consumers. The intelligent systems combine the wavelet transform, Dempster-Shafer evidence theory, voting scheme, fuzzy inference system and artificial neural networks. Results show the efficiency, reliability, and robustness of the proposed methodology, allowing its real-time application.