TITLE:
Detecting and Locating Short-Circuit Faults in Electrical Mesh Networks
AUTHORS:
Nianga-Apila, Mathurin Gogom, Anedi Oko Ganongo, Rodolphe Gomba, Gilbert Ganga
KEYWORDS:
Meshed Electrical Networks, Short-Circuit Faults, Fault Localization, Artificial Intelligence, Artificial Neural Networks (ANNs), Fault Detection and Classification
JOURNAL NAME:
Energy and Power Engineering,
Vol.17 No.6,
June
27,
2025
ABSTRACT: This paper presents a method for detecting, classifying, and locating short-circuit faults in meshed electrical networks using Artificial Neural Networks (ANNs). The proposed approach is applied to a simulated 220 kV Congolese transmission line model developed in MATLAB/SIMULINK. The system uses voltage and current data as input, which are preprocessed through normalization, and is trained using a supervised backpropagation algorithm within a multilayer perceptron architecture. Designed for developing countries, where real-time fault visualization is often limited by the absence of dispatching centers and budgetary constraints, this solution offers a low-cost, autonomous alternative. It can integrate fault localization technologies, such as GPS or fiber optics. The results demonstrate high accuracy, with a mean square error of 2.3001e−17 for fault detection and 3.5313e−18 for fault classification and localization.