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Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines

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  • Lasheen, Ahmed
  • Saad, Mohamed S.
  • Emara, Hassan M.
  • Elshafei, Abdel Latif

Abstract

This paper introduces an extension to continuous time model predictive control to take care of time varying systems by adopting a tube-based approach. The proposed controller can be formulated as a multi-parametric quadratic program that can be solved offline. Therefore, the computational time is reduced. The proposed controller is used to control the pitch angles of large wind turbines in order to regulate the generator speed and power while operating above the turbines rated wind speeds. The proposed controller has two merits. First, it has a fast execution time due to its explicit nature. Second, it is robust against the uncertainties due to the system nonlinearities (as it adopts a tube-based approach). The performance of the proposed controller is compared to a gain-scheduled PI controller which is commonly adopted in industry. Simulation results, based on a benchmark of a typical 5-MW offshore wind turbine, demonstrate the superiority of the proposed predictive controller.

Suggested Citation

  • Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2017. "Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines," Energy, Elsevier, vol. 118(C), pages 1222-1233.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:1222-1233
    DOI: 10.1016/j.energy.2016.11.002
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    References listed on IDEAS

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    1. Eriksson, Sandra & Bernhoff, Hans & Leijon, Mats, 2008. "Evaluation of different turbine concepts for wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(5), pages 1419-1434, June.
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    Citations

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    Cited by:

    1. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2019. "Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 131(C), pages 549-562.
    2. Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
    3. Wan, Xin & Xu, Feng & Luo, Xiong-Lin, 2022. "Economic optimization for process transition based on redundant control variables in the framework of zone model predictive control," Energy, Elsevier, vol. 241(C).
    4. Wan, Xin & Luo, Xiong-Lin, 2020. "Economic optimization of chemical processes based on zone predictive control with redundancy variables," Energy, Elsevier, vol. 212(C).
    5. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
    6. Song, Dongran & Yang, Jian & Su, Mei & Liu, Anfeng & Cai, Zili & Liu, Yao & Joo, Young Hoon, 2017. "A novel wind speed estimator-integrated pitch control method for wind turbines with global-power regulation," Energy, Elsevier, vol. 138(C), pages 816-830.
    7. Jinghan Cui & Su Liu & Jinfeng Liu & Xiangjie Liu, 2018. "A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems," Energies, MDPI, vol. 11(11), pages 1-23, November.
    8. 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.

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