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Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

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  • Kanendra Naidu
  • Mohd Syukri Ali
  • Ab Halim Abu Bakar
  • Chia Kwang Tan
  • Hamzah Arof
  • Hazlie Mokhlis

Abstract

This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0227494
    DOI: 10.1371/journal.pone.0227494
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Kumeshan Reddy & Akshay Kumar Saha, 2022. "An Investigation into the Utilization of Swarm Intelligence for the Design of Dual Vector and Proportional–Resonant Controllers for Regulation of Doubly Fed Induction Generators Subject to Unbalanced ," Energies, MDPI, vol. 15(20), pages 1-36, October.
    2. Rizwan Tariq & Ibrahim Alhamrouni & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq & Nivin A. Ghamry & Habib Hamam, 2022. "An Optimized Solution for Fault Detection and Location in Underground Cables Based on Traveling Waves," Energies, MDPI, vol. 15(17), pages 1-19, September.
    3. Jau-Woei Perng & Yi-Chang Kuo & Yao-Tsung Chang & Hsi-Hsiang Chang, 2020. "Power Substation Construction and Ventilation System Co-Designed Using Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-27, May.

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