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An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

Author

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  • M. S. Abdel Aziz

    (Shaker Consultancy Group, Egypt)

  • M. A. Moustafa Hassan

    (Cairo University, Egypt)

  • E. A. El-Zahab

    (Cairo University, Egypt)

Abstract

This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

Suggested Citation

  • M. S. Abdel Aziz & M. A. Moustafa Hassan & E. A. El-Zahab, 2012. "An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 1(2), pages 44-59, April.
    • Handle: RePEc:igg:jsda00:v:1:y:2012:i:2:p:44-59
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    Cited by:

    1. Kanendra Naidu & Mohd Syukri Ali & Ab Halim Abu Bakar & Chia Kwang Tan & Hamzah Arof & Hazlie Mokhlis, 2020. "Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-22, January.

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