Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data

• Published in: Journal of Applied Science and Engineering Vol. 26; no. 1; pp. 23 - 34
• Main Authors: Felix Ghislain Yem Souhe, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey, Vinny Junior Foba Kakeu
• Format: Journal Article
• Language: English
• Published: 淡江大學 01.01.2023; Tamkang University Press
• Subjects: fault classification; fault detection; fuzzy logic; smart grid; smart meter data; smart grid; fault classification; fault detection; fuzzy logic; smart meter data
• ISSN: 2708-9967; 2708-9975
• DOI: 10.6180/jase.202301_26(1).0003

Fault detection and location give to smart grid the ability to self-healing and isolating the fault in order to limit the negative consequences. In the literature, several techniques are proposed for detection and classification of faults using artificial intelligence algorithms. This paper proposes a novel method using fuzzy logic and neural networks for detection, classification, characterization and location of faults based on data from sensors and smart meters installed in the smart grid. The proposed technique in this paper, use simultaneously the OpenDSS-Matlab platform, makes it possible to detect and classify the fault in the network. The IEEE 37-bus system is used to verify the proposed method. The obtained precision using the proposed strategy is 99.9% which is good value in the literature. This method can be useful for network operators in detection, classification, characterization and location of faults.