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Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization

Author

Listed:
  • Minsheng Yang

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianqi Li

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianying Li

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Xiaofang Yuan

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Jiazhu Xu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Lévy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Lévy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method.

Suggested Citation

  • Minsheng Yang & Jianqi Li & Jianying Li & Xiaofang Yuan & Jiazhu Xu, 2021. "Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization," Energies, MDPI, vol. 14(21), pages 1-15, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7145-:d:669783
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    Citations

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    Cited by:

    1. Matheus Diniz Gonçalves-Leite & Edgar Manuel Carreño-Franco & Jesús M. López-Lezama, 2023. "Impact of Distributed Generation on the Effectiveness of Electric Distribution System Reconfiguration," Energies, MDPI, vol. 16(17), pages 1-20, August.
    2. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
    3. Tianxiang Ma & Ziqi Hu & Yan Xu & Haoran Dong, 2022. "Fault Location Based on Comprehensive Grey Correlation Degree Analysis for Flexible DC Distribution Network," Energies, MDPI, vol. 15(20), pages 1-16, October.
    4. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    5. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP," Energies, MDPI, vol. 16(19), pages 1-25, October.
    6. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.

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