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Two LQRI based Blade Pitch Controls for Wind Turbines

Author

Listed:
  • Sungsu Park

    (Department of Aerospace Engineering, Sejong University, Seoul 143-737, Korea)

  • Yoonsu Nam

    (Department of Mechanical and Mechatronics Engineering, Kangwon National University, Kangwon 200-701, Korea)

Abstract

As the wind turbine size has been increasing and their mechanical components are built lighter, the reduction of the structural loads becomes a very important task of wind turbine control in addition to maximum wind power capture. In this paper, we present a separate set of collective and individual pitch control algorithms. Both pitch control algorithms use the LQR control technique with integral action (LQRI), and utilize Kalman filters to estimate system states and wind speed. Compared to previous works in this area, our pitch control algorithms can control rotor speed and blade bending moments at the same time to improve the trade-off between rotor speed regulation and load reduction, while both collective and individual pitch controls can be designed separately. Simulation results show that the proposed collective and individual pitch controllers achieve very good rotor speed regulation and significant reduction of blade bending moments.

Suggested Citation

  • Sungsu Park & Yoonsu Nam, 2012. "Two LQRI based Blade Pitch Controls for Wind Turbines," Energies, MDPI, vol. 5(6), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:6:p:1998-2016:d:18445
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    Citations

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    Cited by:

    1. 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.
    2. Taesu Jeon & Insu Paek, 2021. "Design and Verification of the LQR Controller Based on Fuzzy Logic for Large Wind Turbine," Energies, MDPI, vol. 14(1), pages 1-17, January.
    3. Kwansu Kim & Hyun-Gyu Kim & Yuan Song & Insu Paek, 2019. "Design and Simulation of an LQR-PI Control Algorithm for Medium Wind Turbine," Energies, MDPI, vol. 12(12), pages 1-18, June.
    4. Abhinandan Routray & Nitin Sivakumar & Sung-ho Hur & Deok-je Bang, 2023. "A Comparative Study of Optimal Individual Pitch Control Methods," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    5. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    6. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    7. Jongmin Cheon & Jinwook Kim & Joohoon Lee & Kichang Lee & Youngkiu Choi, 2019. "Development of Hardware-in-the-Loop-Simulation Testbed for Pitch Control System Performance Test," Energies, MDPI, vol. 12(10), pages 1-20, May.
    8. Nejra Beganovic & Jackson G. Njiri & Dirk Söffker, 2018. "Reduction of Structural Loads in Wind Turbines Based on an Adapted Control Strategy Concerning Online Fatigue Damage Evaluation Models," Energies, MDPI, vol. 11(12), pages 1-15, December.
    9. Arash E. Samani & Jeroen D. M. De Kooning & Nezmin Kayedpour & Narender Singh & Lieven Vandevelde, 2020. "The Impact of Pitch-To-Stall and Pitch-To-Feather Control on the Structural Loads and the Pitch Mechanism of a Wind Turbine," Energies, MDPI, vol. 13(17), pages 1-21, September.
    10. Xingqi Hu & Wen Tan & Guolian Hou, 2023. "PIDD2 Control of Large Wind Turbines’ Pitch Angle," Energies, MDPI, vol. 16(13), pages 1-22, July.
    11. Njiri, Jackson G. & Söffker, Dirk, 2016. "State-of-the-art in wind turbine control: Trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 377-393.

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