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Model predictive control with finite control set for variable-speed wind turbines

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  • Song, Dongran
  • Yang, Jian
  • Dong, Mi
  • Joo, Young Hoon

Abstract

The existing model predictive control (MPC) algorithm for variable-speed wind turbines (WTs) is using continuous control set and solved by a quadratic programming method. Its main drawbacks are the heavily computational burden and the difficulty to implement. This paper introduces an alternative MPC method by using finite control set, which is used in controlling WTs at the first attempt. To do this, first of all, the WT's nonlinear model is linearized with information provided by a non-standard extended Kalman filter. Secondly, a discrete-time linear model of the system is used to predict the future value of the interested state variable for possible control sets. In view of the fact that control objectives are different within two operation zones partitioned by wind speed, two quality functions are predefined. One quality function evaluates the optimal generator speed tracking error together with the penalty of torque actuator action at below rated wind speed, while the other evaluates the rated generator speed tracking error and the penalty of pitch actuator action at above rated wind speed. Then, the corresponding control set which minimizes the quality function is selected. Finally, some simulation results are demonstrated to visualize the effectiveness and feasibility of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:564-572
    DOI: 10.1016/j.energy.2017.02.149
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    Cited by:

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    15. Li, Qing’an & Xu, Jianzhong & Kamada, Yasunari & Takao, Maeda & Nishimura, Shogo & Wu, Guangxing & Cai, Chang, 2020. "Experimental investigations of airfoil surface flow of a horizontal axis wind turbine with LDV measurements," Energy, Elsevier, vol. 191(C).
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    18. Tingrui Liu & Kang Zhao & Changle Sun & Jiahao Jia & Guifang Liu, 2020. "Real-Time Flow Control of Blade Section Using a Hydraulic Transmission System Based on an H-Inf Controller with LMI Design," Energies, MDPI, vol. 13(19), pages 1-16, September.
    19. Wakui, Tetsuya & Yoshimura, Motoki & Yokoyama, Ryohei, 2017. "Multiple-feedback control of power output and platform pitching motion for a floating offshore wind turbine-generator system," Energy, Elsevier, vol. 141(C), pages 563-578.
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    22. Masood, Nahid-Al- & Mahmud, Sajjad Uddin & Ansary, Md Nazmuddoha & Deeba, Shohana Rahman, 2022. "Improvement of system strength under high wind penetration: A techno-economic assessment using synchronous condenser and SVC," Energy, Elsevier, vol. 246(C).
    23. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
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