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Pouabe Eboule, P.S., Pretorius, J.H.C. and Mbuli, N. (2018) Artificial Neural Network Techniques Apply for Fault Detecting and Locating in Overhead Power Transmission Line. 2018 Australasian Universities Power Engineering Conference (AUPEC), Auckland, 27-30 November 2018, 1-6.
https://doi.org/10.1109/aupec.2018.8757959

has been cited by the following article:

• 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.