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Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC

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  • Yao, Qi
  • Hu, Yang
  • Zhao, Tianyang
  • Guan, Yuanpeng
  • Luo, Zhiling
  • Liu, Jizhen

Abstract

Since the established wind farms (WFs) have a strong demand for reducing maintenance costs, the fatigue load suppression of wind turbines (WTs) has become more important in WFs. To suppress the fatigue load during operation, the traditional schemes are tried by allocating the optimized power setting value to each WT, which does not directly point to the key operating parameters that affect fatigue. In response to this, an advanced WT control model with two-degree-of-freedom (2Dof) is applied in this paper to ensure that the generator torque and pitch angle (two key parameters affecting fatigue load) of each WT are directly controlled. In order to achieve effective control of the above two parameters in multiple WTs, a hierarchical distributed control architecture with dynamic-local-reference is proposed to jointly optimize the above two configurable parameters at both the WT and WF levels. Several cases are compared to verify the proposed method, and simulation results show that the proposed 2Dof control model for WT and distributed control framework for WF can significantly reduce the fatigue load of each WT while completing the active power command of the whole WF.

Suggested Citation

  • Yao, Qi & Hu, Yang & Zhao, Tianyang & Guan, Yuanpeng & Luo, Zhiling & Liu, Jizhen, 2022. "Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC," Renewable Energy, Elsevier, vol. 183(C), pages 423-434.
    • Handle: RePEc:eee:renene:v:183:y:2022:i:c:p:423-434
    DOI: 10.1016/j.renene.2021.10.069
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Song, Dongran & Tu, Yanping & Wang, Lei & Jin, Fangjun & Li, Ziqun & Huang, Chaoneng & Xia, E & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Hoon Joo, Young, 2022. "Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator," Applied Energy, Elsevier, vol. 312(C).
    2. Chen, Peng & Han, Dezhi, 2023. "Reward adaptive wind power tracking control based on deep deterministic policy gradient," Applied Energy, Elsevier, vol. 348(C).
    3. Bai, Guan & Feng, Yaojing & Ma, Zi-Qian & Li, Xueping, 2024. "An asynchronous distributed optimal wake control scheme for suppressing fatigue load and increasing power extraction in wind farms," Renewable Energy, Elsevier, vol. 232(C).
    4. Wang, Wenwen & Kong, Xiaobing & Li, Gangqiang & Liu, Xiangjie & Ma, Lele & Liu, Wenting & Lee, Kwang Y., 2024. "Wind farm control using distributed economic MPC scheme under the influence of wake effect," Energy, Elsevier, vol. 309(C).

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