%0 Journal Article
%T Detecting and Locating Short-Circuit Faults in Electrical Mesh Networks
%A Nianga-Apila &nbsp
%A Mathurin Gogom
%A Anedi Oko Ganongo
%A Rodolphe Gomba
%A Gilbert Ganga
%J Energy and Power Engineering
%P 134-153
%@ 1947-3818
%D 2025
%I Scientific Research Publishing
%R 10.4236/epe.2025.176007
%X 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.
%K Meshed Electrical Networks
%K Short-Circuit Faults
%K Fault Localization
%K Artificial Intelligence
%K Artificial Neural Networks (ANNs)
%K Fault Detection and Classification
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=143610