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Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions

Author

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  • Xiaobing Kong

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Lele Ma

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Xiangjie Liu

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Mohamed Abdelkarim Abdelbaky

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Electrical Power and Machines Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt)

  • Qian Wu

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

With the gradual increase in the installed capacity of wind turbines, more and more attention has been paid to the economy of wind power. Economic model-predictive control (EMPC) has been developed as an effective advanced control strategy, which can improve the dynamic economy performance of the system. However, the variable-speed wind turbine (VSWT) system widely used is generally nonlinear and highly coupled nonaffine systems, containing multiple economic terms. Therefore, a nonlinear EMPC strategy considering power maximization and mechanical load minimization is proposed based on the comprehensive VSWT model, including the dynamics of the tower and the gearbox in this paper. Three groups of simulations verify the effectiveness and reliability/practicability of the proposed nonlinear EMPC strategy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:184-:d:304011
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).
    3. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    4. Teng, Yiming & Hu, Dewen & Wu, Feng & Zhang, Ridong & Gao, Furong, 2020. "Fast economic model predictive control for marine current turbine generator system," Renewable Energy, Elsevier, vol. 166(C), pages 108-116.
    5. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
    6. José Antonio Cortajarena & Oscar Barambones & Patxi Alkorta & Jon Cortajarena, 2021. "Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid," Mathematics, MDPI, vol. 9(2), pages 1-18, January.
    7. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    8. Pustina, L. & Biral, F. & Serafini, J., 2022. "A novel Economic Nonlinear Model Predictive Controller for power maximisation on wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    9. Francesco Castellani & Davide Astolfi, 2020. "Editorial on Special Issue “Wind Turbine Power Optimization Technology”," Energies, MDPI, vol. 13(7), pages 1-4, April.
    10. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    11. Jie Bao & Hong Yue, 2022. "Design and Assessment of a LIDAR-Based Model Predictive Wind Turbine Control," Energies, MDPI, vol. 15(17), pages 1-19, September.
    12. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
    13. Kong, Xiaobing & Ma, Lele & Wang, Ce & Guo, Shifan & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2022. "Large-scale wind farm control using distributed economic model predictive scheme," Renewable Energy, Elsevier, vol. 181(C), pages 581-591.
    14. Stefano Dettori & Alessandro Maddaloni & Filippo Galli & Valentina Colla & Federico Bucciarelli & Damaso Checcacci & Annamaria Signorini, 2021. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control," Energies, MDPI, vol. 14(13), pages 1-30, July.
    15. Atsushi Yamaguchi & Iman Yousefi & Takeshi Ishihara, 2020. "Reduction in the Fluctuating Load on Wind Turbines by Using a Combined Nacelle Acceleration Feedback and Lidar-Based Feedforward Control," Energies, MDPI, vol. 13(17), pages 1-18, September.

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