Felix Ghislain Yem Souhe This email address is being protected from spambots. You need JavaScript enabled to view it.1, Alexandre Teplaira Boum1, Pierre Ele2, Camille Franklin Mbey1, and Vinny Junior Foba Kakeu1

1Department of Electrical Engineering, ENSET, University of Douala, Cameroon
2Department of Electrical Engineering, Polytechnic of Yaounde, Yaounde, Cameroon


Received: September 24, 2021
Accepted: February 9, 2022
Publication Date: March 18, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/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.

Keywords: fault classification, fault detection, fuzzy logic, smart meter data, smart grid


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