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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

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