Intelligent Fault Diagnosis in a Power Distribution Network | Zendy

Oluleke Babayomi | Zendy; Peter Olabisi Oluseyi | Zendy

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Intelligent Fault Diagnosis in a Power Distribution Network

Author(s) -

Oluleke Babayomi,

Peter Olabisi Oluseyi

Publication year - 2016

Publication title -

advances in electrical engineering

Language(s) - English

Resource type - Journals

eISSN - 2356-6655

pISSN - 2314-7636

DOI - 10.1155/2016/8651630

Subject(s) - fault (geology) , fault indicator , line (geometry) , identification (biology) , artificial neural network , power (physics) , computer science , fuzzy logic , fault model , high impedance , electric power system , fault coverage , engineering , pattern recognition (psychology) , electrical impedance , fault detection and isolation , artificial intelligence , electrical engineering , mathematics , botany , geometry , physics , quantum mechanics , seismology , electronic circuit , actuator , biology , geology

This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults

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