Artificial Neural Network Applications in Transmission Line Fault Diagnosis

Obinna Kingsley Obi., Chinedu Chigozie Nwobu., Abigail Chidimma Odigbo. and Dennis Chukwuemeka Oyiogu
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Obinna Kingsley Obi.: Department of Electrical Engineering, Nnamdi Azikiwe University Awka, Nigeria
Chinedu Chigozie Nwobu.: Department of Electrical Engineering, Nnamdi Azikiwe University Awka, Nigeria
Abigail Chidimma Odigbo.: Department of Electrical Engineering, Nnamdi Azikiwe University Awka, Nigeria
Dennis Chukwuemeka Oyiogu: Department of Electrical Engineering, Nnamdi Azikiwe University Awka, Nigeria

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 8, 48-62

Abstract: This study proposes an intelligent fault detection mechanism using artificial neural networks (ANNs) to detect faults on power system transmission lines. A prototype of Kaduna-to-Kano transmission line network was modeled in Simulink, and voltage and current data were extracted and trained using the Levenberg-Marquardt backpropagation algorithm. The results show that the ANN can detect both symmetrical and non-symmetrical faults, with validation plots and regression plots demonstrating its effectiveness. This technique is highly recommended for power system transmission line networks and can be extended to distribution networks.

Date: 2024
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