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

This research focused on identifying various types of faults occurring on 330kV transmission lines through the use of artificial neural networks (ANN). Four types of faults were considered, along with a fifth condition representing no fault.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 A MATLAB model for the Gwagwalada-Katampe 330kV transmission line in Nigeria was implemented to generate fault datasets.
• 2 Voltage and current fault parameters were utilized to train and simulate the ANN network architecture selected for each stage of fault detection.
• 3 Four types of faults were considered, along with a fifth condition representing no fault.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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