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Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm

Author

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  • Yi Zhang

    (College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130000, China
    Key Laboratory for Comprehensive Energy Saving of Cold Regions Architecture of Ministry of Education, Jilin Jianzhu University, Changchun 130118, China)

  • Pengtao Liu

    (College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130000, China
    Key Laboratory for Comprehensive Energy Saving of Cold Regions Architecture of Ministry of Education, Jilin Jianzhu University, Changchun 130118, China)

Abstract

This paper presents an improved osprey optimization algorithm (IOOA) to solve the problems of slow convergence and local optimality. First, the osprey population is initialized based on the Sobol sequence to increase the initial population’s diversity. Second, the step factor, based on Weibull distribution, is introduced in the osprey position updating process to balance the explorative and developmental ability of the algorithm. Lastly, a disturbance based on the Firefly Algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. By mixing three improvement strategies, the performance of the original algorithm has been comprehensively improved. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of the local optimal, and higher robustness. In addition, we also applied IOOA to the reactive power optimization problem of IEEE33 and IEEE69 node, and the active power network loss was reduced by 48.7% and 42.1%, after IOOA optimization, respectively, which verifies the feasibility and effectiveness of IOOA in solving practical problems.

Suggested Citation

  • Yi Zhang & Pengtao Liu, 2023. "Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm," Energies, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7101-:d:1260204
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    References listed on IDEAS

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    1. Xiang, Yue & Zhou, Lili & Huang, Yuan & Zhang, Xin & Liu, Youbo & Liu, Junyong, 2021. "Reactive coordinated optimal operation of distributed wind generation," Energy, Elsevier, vol. 218(C).
    2. Li, Maodong & Xu, Guanghui & Lai, Qiang & Chen, Jie, 2022. "A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 71-99.
    3. Minsheng Yang & Jianqi Li & Rui Du & Jianying Li & Jian Sun & Xiaofang Yuan & Jiazhu Xu & Shifu Huang, 2022. "Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization," Energies, MDPI, vol. 15(6), pages 1-16, March.
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