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Artificial Neural Network Applications in Transmission Line Fault Diagnosis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Obinna Kingsley Obi. & Chinedu Chigozie Nwobu. & Abigail Chidimma Odigbo. & Dennis Chukwuemeka Oyiogu, 2024. "Artificial Neural Network Applications in Transmission Line Fault Diagnosis," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 9(8), pages 48-62, August.
    • Handle: RePEc:bjf:journl:v:9:y:2024:i:8:p:48-62
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