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Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization

Author

Listed:
  • Xinghua Tao

    (School of Intelligent Manufacturing, Nanning University, Nanning 530100, China)

  • Nan Mo

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Jianbo Qin

    (School of Intelligent Manufacturing, Nanning University, Nanning 530100, China)

  • Xiaozhe Yang

    (School of Intelligent Manufacturing, Nanning University, Nanning 530100, China)

  • Linfei Yin

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Likun Hu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.

Suggested Citation

  • Xinghua Tao & Nan Mo & Jianbo Qin & Xiaozhe Yang & Linfei Yin & Likun Hu, 2023. "Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization," Energies, MDPI, vol. 16(19), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6982-:d:1254960
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    References listed on IDEAS

    as
    1. S. Gratton & Ph. L. Toint, 2020. "A note on solving nonlinear optimization problems in variable precision," Computational Optimization and Applications, Springer, vol. 76(3), pages 917-933, July.
    2. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
    3. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    4. Yang, Bo & Yu, Tao & Shu, Hongchun & Dong, Jun & Jiang, Lin, 2018. "Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers," Applied Energy, Elsevier, vol. 210(C), pages 711-723.
    5. Stavros P. Adam & Stamatios-Aggelos N. Alexandropoulos & Panos M. Pardalos & Michael N. Vrahatis, 2019. "No Free Lunch Theorem: A Review," Springer Optimization and Its Applications, in: Ioannis C. Demetriou & Panos M. Pardalos (ed.), Approximation and Optimization, pages 57-82, Springer.
    6. Xu, Xiaomin & Niu, Dongxiao & Xiao, Bowen & Guo, Xiaodan & Zhang, Lihui & Wang, Keke, 2020. "Policy analysis for grid parity of wind power generation in China," Energy Policy, Elsevier, vol. 138(C).
    7. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, June.
    8. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    Full references (including those not matched with items on IDEAS)

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