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Multivariable robust blade pitch control design to reject periodic loads on wind turbines

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  • Yuan, Yuan
  • Chen, Xu
  • Tang, J.

Abstract

The demand on sustainable operations of large-scale wind turbines necessitates the concurrent advancement of power regulation and load mitigation through blade pitch control. Traditional collective pitch control (CPC) mechanisms can only deal with symmetric disturbances. The advent of individual pitch control (IPC) provides new opportunities to mitigate asymmetric or periodic loads on blades. Nevertheless, difficulties in control synthesis remain. In order for IPC to be truly effective, the complicated dynamic coupling between turbine components has to be accounted for. Moreover, wind turbine dynamics is highly nonlinear, and significant modeling uncertainties exist. In this research, a multivariable robust IPC framework is developed, aiming at rejecting periodic loads. The inter-blade coupling is explicitly modeled to provide response characteristics in the frequency domain. Subsequently, the structured singular values (μ)-synthesis strategy is adopted, as it shows distinct capability of dealing with periodic loads. In particular, weighting functions can be tailored to suppress response peaks at periodic frequencies with guaranteed robustness. Systematic case investigations indicate that, with the proposed IPC strategy, one can achieve significant periodic load mitigation as well as fatigue alleviation in speed-varying wind fields.

Suggested Citation

  • Yuan, Yuan & Chen, Xu & Tang, J., 2020. "Multivariable robust blade pitch control design to reject periodic loads on wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 329-341.
  • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:329-341
    DOI: 10.1016/j.renene.2019.06.136
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    References listed on IDEAS

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    1. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    2. Odgaard, Peter Fogh & Larsen, Lars F.S. & Wisniewski, Rafael & Hovgaard, Tobias Gybel, 2016. "On using Pareto optimality to tune a linear model predictive controller for wind turbines," Renewable Energy, Elsevier, vol. 87(P2), pages 884-891.
    3. Bououden, S. & Chadli, M. & Filali, S. & El Hajjaji, A., 2012. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach," Renewable Energy, Elsevier, vol. 37(1), pages 434-439.
    4. Petrović, Vlaho & Jelavić, Mate & Baotić, Mato, 2015. "Advanced control algorithms for reduction of wind turbine structural loads," Renewable Energy, Elsevier, vol. 76(C), pages 418-431.
    5. Asier Diaz De Corcuera & Aron Pujana-Arrese & Jose M. Ezquerra & Edurne Segurola & Joseba Landaluze, 2012. "H ∞ Based Control for Load Mitigation in Wind Turbines," Energies, MDPI, vol. 5(4), pages 1-30, April.
    6. Hassan, H.M. & ElShafei, A.L. & Farag, W.A. & Saad, M.S., 2012. "A robust LMI-based pitch controller for large wind turbines," Renewable Energy, Elsevier, vol. 44(C), pages 63-71.
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    1. Hawari, Qusay & Kim, Taeseong & Ward, Christopher & Fleming, James, 2022. "A robust gain scheduling method for a PI collective pitch controller of multi-MW onshore wind turbines," Renewable Energy, Elsevier, vol. 192(C), pages 443-455.
    2. Tang, Shize & Tian, De & Wu, Xiaoxuan & Huang, Mingyue & Deng, Ying, 2022. "Wind turbine load reduction based on 2DoF robust individual pitch control," Renewable Energy, Elsevier, vol. 183(C), pages 28-40.
    3. Tong, Xin & Zhao, Xiaowei, 2021. "Vibration and power regulation control of a floating wind turbine with hydrostatic transmission," Renewable Energy, Elsevier, vol. 167(C), pages 899-906.
    4. Dali, Ali & Abdelmalek, Samir & Bakdi, Azzeddine & Bettayeb, Maamar, 2021. "A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine," Renewable Energy, Elsevier, vol. 172(C), pages 1021-1034.
    5. Pan, Lin & Wang, Xudong, 2020. "Variable pitch control on direct-driven PMSG for offshore wind turbine using Repetitive-TS fuzzy PID control," Renewable Energy, Elsevier, vol. 159(C), pages 221-237.
    6. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Daniel Teso-Fz-Betoño & Javier Olarte, 2020. "Kharitonov Theorem Based Robust Stability Analysis of a Wind Turbine Pitch Control System," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    7. Jia, Chengzhen & Wang, Lingmei & Meng, Enlong & Chen, Liming & Liu, Yushan & Jia, Wenqiang & Bao, Yutao & Liu, Zhenguo, 2021. "Combining LIDAR and LADRC for intelligent pitch control of wind turbines," Renewable Energy, Elsevier, vol. 169(C), pages 1091-1105.
    8. Li, Jianshen & Wang, Shuangxin, 2021. "Dual multivariable model-free adaptive individual pitch control for load reduction in wind turbines with actuator faults," Renewable Energy, Elsevier, vol. 174(C), pages 293-304.

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