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Enhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence

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  • Okwudili E. Obi
  • Oseloka A. Ezechukwu
  • Chukwuedozie N. Ezema

Abstract

This paper is on "Enhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence". The actual implementation and development of the neural networks and their architectures have been achieved for the three different parts of the fault diagnosis process namely fault detection, classification and fault location. This paper also gives an overview of the training and testing processes employed with neural networks. Series of simulation results that have been obtained using MATLAB, Sim Power Systems and the Artificial Neural Networks Toolboxes in Simulink are also presented in detail to emphasize the efficiency and accuracy factors of the proposed fault locator. Several neural networks with varying configurations have been trained, tested and their performances have been analyzed in this work.

Suggested Citation

  • Okwudili E. Obi & Oseloka A. Ezechukwu & Chukwuedozie N. Ezema, 2016. "Enhanced Real Time and Off-Line Transmission Line Fault Diagnosis Using Artificial Intelligence," Asian Journal of Computing and Engineering Technology, IPRJB, vol. 1(1), pages 1-22.
    • Handle: RePEc:bdu:oajcet:v:1:y:2016:i:1:p:1-22:id:215
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