Design and Simulation of a Faults Prediction and Localization Model on Transmission Line Using Artificial Neural Network

This study presents the design and development of a Fault Prediction and Localization Model for transmission lines using Artificial Neural Networks (ANNs). The model predicts, localizes, and notifies precise fault locations, enhancing the reliability and efficiency of power transmission systems. The research work focuses on modeling the NIPP 218 km transmission line from Egbin to Benin and utilizing an artificial neural network (ANN) with back propagation to identify and locate various faults on the transmission line. Different faults including single line to ground, double line to ground, and balanced faults were simulated at a distance of 20 km from the sending end. The ANN was trained to identify the type of fault and predict its location. MATLAB SIMULINK was used to simulate the system. Results show that the ANN correctly identified all fault types and predicted fault locations with an accuracy of approximately 99.55%. The research demonstrates the capability of ANNs in fault identification and location prediction in power transmission lines when properly trained.