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Distribution Network Reconfiguration Optimization Using a New Algorithm Hyperbolic Tangent Particle Swarm Optimization (HT-PSO)

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Listed:
  • David W. Puma

    (Faculty of Mechanical Engineering, National University of Engineering, Lima 15333, Peru
    Facultad de Ingeniería Eléctrica y de Potencia, Universidad Tecnológica del Perú, Lima 15306, Peru)

  • Y. P. Molina

    (Department of Electrical Engineering, Federal University of Paraíba, Joao Pessoa 58051-900, PB, Brazil)

  • Brayan A. Atoccsa

    (Facultad de Ingeniería Eléctrica y de Potencia, Universidad Tecnológica del Perú, Lima 15306, Peru)

  • J. E. Luyo

    (Faculty of Mechanical Engineering, National University of Engineering, Lima 15333, Peru)

  • Zocimo Ñaupari

    (Faculty of Electrical Engineering, National University of Engineering, Lima 15333, Peru)

Abstract

This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ , into the HT-PSO, enhancing the curve’s adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3798-:d:1448295
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    References listed on IDEAS

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    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. Abdulaziz Alanazi & Tarek I. Alanazi, 2023. "Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    3. Yinghao Shan & Liqian Ma & Xiangkai Yu, 2023. "Hierarchical Control and Economic Optimization of Microgrids Considering the Randomness of Power Generation and Load Demand," Energies, MDPI, vol. 16(14), pages 1-23, July.
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