Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line

TLDR

The study aims to detect various fault types on 330 kV Nigerian transmission lines using artificial neural networks. A MATLAB model of the Gwagwalada‑Katampe line generated fault datasets, and voltage and current parameters trained an ANN to recognize four fault types plus a no‑fault condition. The ANN successfully identified the fault conditions and system parameters on the modeled line.

Abstract

This research focused on identifying various types of faults occurring on 330kV transmission lines through the use of artificial neural networks (ANN). A MATLAB model for the Gwagwalada-Katampe 330kV transmission line in Nigeria was implemented to generate fault datasets. Voltage and current fault parameters were utilized to train and simulate the ANN network architecture selected for each stage of fault detection. Four types of faults were considered, along with a fifth condition representing no fault. The results illustrated the success of the developed model in identifying various fault conditions and system parameters on the Gwagwalada-Katampe 330kV transmission line, modelled using MATLAB Simulink.