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An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization

Author

Listed:
  • Fengli Jiang

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yichi Zhang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Yu Zhang

    (Anhui Provincial Laboratory of New Energy Utilization and Energy Conservation, Hefei University Technology, Hefei 230009, China)

  • Xiaomeng Liu

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Chunling Chen

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative particles and chaos particles were introduced into the population of the developed algorithm, which was to decrease the randomness and promote search efficiency through guiding particle position updating. Moreover, the cluster focus distance-changing rate was responsible for dynamically adjusting inertia weight. Then the convergence rate and accuracy of this algorithm would be elevated by four functions, which would test effectively the proposed. Finally, the optimized algorithm was verified on the RPO of the IEEE 30-bus power system. The performance of PSO, Random weight particle swarm optimization (WPSO) and Linearly decreasing weight of the particle swarm optimization algorithm (LDWPSO) were identified as the referential information, the proposed GSAPSO was more efficient from the comparison. Calculation results demonstrated that higher quality solutions were obtained and convergence rate and accuracy was significantly higher with regard to the GSAPSO algorithm.

Suggested Citation

  • Fengli Jiang & Yichi Zhang & Yu Zhang & Xiaomeng Liu & Chunling Chen, 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization," Energies, MDPI, vol. 12(9), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1690-:d:228295
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    References listed on IDEAS

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    2. Niknam, Taher & Firouzi, Bahman Bahmani & Ostadi, Amir, 2010. "A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators," Applied Energy, Elsevier, vol. 87(6), pages 1919-1928, June.
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    Cited by:

    1. Peng Cheng & Zhiyu Xu & Ruiye Li & Chao Shi, 2022. "A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources," Energies, MDPI, vol. 15(13), pages 1-16, June.
    2. Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
    3. Seok-Il Go & Sang-Yun Yun & Seon-Ju Ahn & Joon-Ho Choi, 2020. "Voltage and Reactive Power Optimization Using a Simplified Linear Equations at Distribution Networks with DG," Energies, MDPI, vol. 13(13), pages 1-23, June.
    4. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2019. "Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms," Energies, MDPI, vol. 12(22), pages 1-36, November.
    5. Hao He & Jia Li & Weizhe Zhao & Boyang Li & Yalong Li, 2022. "Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm," Energies, MDPI, vol. 15(10), pages 1-12, May.

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