Article citationsMore>>

Vasilic, S. and Kezunovic, M. (2002) An Improved Neural Network Algorithm for Classifying the Transmission Line Faults. 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), New York, 27-31 January 2002, 918-923.
https://doi.org/10.1109/pesw.2002.985139

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.