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Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis

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  • Lin, Zhongwei
  • Chen, Zhenyu
  • Wu, Qiuwei
  • Yang, Shuo
  • Meng, Hongmin

Abstract

For the existing pitch and torque control of the wind turbine generator system (WTGS), further development on coordinated control is necessary to improve effectiveness for practical applications. In this paper, the WTGS is modeled as a coupling combination of two subsystems: the generator torque control subsystem and blade pitch control subsystem. Then, the pole positions in each control subsystem are adjusted coordinately to evaluate the controller participation and used as the objective of optimization. A two-level parameters-controllers coordinated optimization scheme is proposed and applied to optimize the controller coordination based on the Pareto optimization theory. Three solutions are obtained through optimization, which includes the optimal torque solution, optimal power solution, and satisfactory solution. Detailed comparisons evaluate the performance of the three selected solutions and provide the optimized controller coordination suggestions according to different requirements.

Suggested Citation

  • Lin, Zhongwei & Chen, Zhenyu & Wu, Qiuwei & Yang, Shuo & Meng, Hongmin, 2018. "Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis," Energy, Elsevier, vol. 147(C), pages 812-825.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:812-825
    DOI: 10.1016/j.energy.2018.01.055
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    References listed on IDEAS

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    Cited by:

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    2. Xie, Jingjie & Dong, Hongyang & Zhao, Xiaowei, 2023. "Data-driven torque and pitch control of wind turbines via reinforcement learning," Renewable Energy, Elsevier, vol. 215(C).
    3. Chen, Zhenyu & Lin, Zhongwei & Ren, Xin & Chen, Kaixuan & Zhang, Guangming & Xie, Zhen & Wang, Chuanxi & She, Chao, 2023. "Amplitude-optimized Koopman-linear flow estimator for wind turbine wake dynamics: Approximation, prediction and reconstruction," Energy, Elsevier, vol. 263(PE).
    4. Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
    5. 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.
    6. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).

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