Open AccessFuzzy based methodologies comparison for high‐impedance fault diagnosis in radial distribution feeders
Author(s) -
TonelliNeto Mauro S.,
Decanini José Guilherme M.S.,
Lotufo Anna Diva P.,
Minussi Carlos Roberto
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1409
Subject(s) - robustness (evolution) , fuzzy logic , computer science , artificial neural network , electric power system , reliability engineering , fuzzy inference system , wavelet transform , reliability (semiconductor) , inference , wavelet , artificial intelligence , engineering , adaptive neuro fuzzy inference system , power (physics) , fuzzy control system , biochemistry , chemistry , physics , quantum mechanics , gene
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.