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Distribution Network Reconfiguration Based on an Improved Arithmetic Optimization Algorithm

Author

Listed:
  • Hui Jia

    (Faculty of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Xueling Zhu

    (Faculty of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Wensi Cao

    (Faculty of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

Aiming to address the defects of the arithmetic optimization algorithm (AOA), such as easy fall into local optimums and slow convergence speed during the search process, an improved arithmetic optimization algorithm (IAOA) is proposed and applied to the study of distribution network reconfiguration. Firstly, a reconfiguration model is established to reduce network loss, and a cosine control factor is introduced to reconfigure the math optimization accelerated (MOA) function to coordinate the algorithm’s global exploration and local exploitation capabilities. Subsequently, a reverse differential evolution strategy is introduced to improve the overall diversity of the population and Weibull mutation is performed on the better-adapted individuals generated in each iteration to ensure the quality of the optimal individuals generated in each iteration and strengthen the algorithm’s ability to approach the optimal solution. The performance of the improved algorithm is also tested using eight basis functions. Finally, simulation analysis is carried out by taking the IEEE33 and IEEE69 node systems and a real power distribution system as examples; the results show that the proposed algorithm can help to reconfigure the system quickly, and the system node voltages and network losses were significantly improved after the reconfiguration.

Suggested Citation

  • Hui Jia & Xueling Zhu & Wensi Cao, 2024. "Distribution Network Reconfiguration Based on an Improved Arithmetic Optimization Algorithm," Energies, MDPI, vol. 17(8), pages 1-15, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1969-:d:1379883
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    References listed on IDEAS

    as
    1. Yanmin Wu & Jiaqi Liu & Lu Wang & Yanjun An & Xiaofeng Zhang, 2023. "Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion Optimization Algorithm," Energies, MDPI, vol. 16(20), pages 1-17, October.
    2. Mohammed Alqahtani & Ponnusamy Marimuthu & Veerasamy Moorthy & B. Pangedaiah & Ch. Rami Reddy & M. Kiran Kumar & Muhammad Khalid, 2023. "Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization," Energies, MDPI, vol. 16(12), pages 1-15, June.
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