IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i8p1969-d1379883.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/8/1969/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/8/1969/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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 1-19, February.

    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:17:y:2024:i:8:p:1969-:d:1379883. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.