IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7185-d1264498.html
   My bibliography  Save this article

Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion Optimization Algorithm

Author

Listed:
  • Yanmin Wu

    (College of Electric Engineering, Naval University of Engineering, Wuhan 430033, China
    College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Jiaqi Liu

    (College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Lu Wang

    (College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Yanjun An

    (College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Xiaofeng Zhang

    (College of Electric Engineering, Naval University of Engineering, Wuhan 430033, China)

Abstract

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion Optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved; the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, more economical and reliable distribution network.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7185-:d:1264498
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7185/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7185/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David W. Puma & Y. P. Molina & Brayan A. Atoccsa & J. E. Luyo & Zocimo Ñaupari, 2024. "Distribution Network Reconfiguration Optimization Using a New Algorithm Hyperbolic Tangent Particle Swarm Optimization (HT-PSO)," Energies, MDPI, vol. 17(15), pages 1-13, August.
    2. Wei-Chen Lin & Chao-Hsien Hsiao & Wei-Tzer Huang & Kai-Chao Yao & Yih-Der Lee & Jheng-Lun Jian & Yuan Hsieh, 2024. "Network Reconfiguration Framework for CO 2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms," Sustainability, MDPI, vol. 16(4), pages 1-17, February.
    3. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7185-:d:1264498. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.