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Design and Simulation of an LQR-PI Control Algorithm for Medium Wind Turbine

Author

Listed:
  • Kwansu Kim

    (Department of Advanced Mechanical Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

  • Hyun-Gyu Kim

    (Department of Advanced Mechanical Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

  • Yuan Song

    (Department of Advanced Mechanical Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

  • Insu Paek

    (Division of Mechanical and Biomedical, Mechatronics and Materials Science and Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

Abstract

In this paper, a new linear quadratic regulator (LQR) and proportional integral (PI) hybrid control algorithm for a permanent-magnet synchronous-generator (PMSG) horizontal-axis wind turbine was developed and simulated. The new algorithm incorporates LQR control into existing PI control structures as a feed-forward term to improve the performance of a conventional PI control. A numerical model based on MATLAB/Simulink and a commercial aero-elastic code were constructed for the target wind turbine, and the new control technique was applied to the numerical model to verify the effect through simulation. For the simulation, the performance data were compared after applying the PI, LQR, and LQR-PI control algorithms to the same wind speed conditions with and without noise in the generator speed. Also, the simulations were performed in both the transition region and the rated power region. The LQR-PI algorithm was found to reduce the standard deviation of the generator speed by more than 20% in all cases regardless of the noise compared with the PI algorithm. As a result, the proposed LQR-PI control increased the stability of the wind turbine in comparison with the conventional PI control.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2248-:d:239261
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    References listed on IDEAS

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    1. Bottasso, C.L. & Croce, A. & Nam, Y. & Riboldi, C.E.D., 2012. "Power curve tracking in the presence of a tip speed constraint," Renewable Energy, Elsevier, vol. 40(1), pages 1-12.
    2. Sungsu Park & Yoonsu Nam, 2012. "Two LQRI based Blade Pitch Controls for Wind Turbines," Energies, MDPI, vol. 5(6), pages 1-19, June.
    3. Bottasso, C.L. & Riboldi, C.E.D., 2015. "Validation of a wind misalignment observer using field test data," Renewable Energy, Elsevier, vol. 74(C), pages 298-306.
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    Cited by:

    1. Gerardo Humberto Valencia-Rivera & Ivan Amaya & Jorge M. Cruz-Duarte & José Carlos Ortíz-Bayliss & Juan Gabriel Avina-Cervantes, 2021. "Hybrid Controller Based on LQR Applied to Interleaved Boost Converter and Microgrids under Power Quality Events," Energies, MDPI, vol. 14(21), pages 1-31, October.
    2. Janusz Baran & Andrzej Jąderko, 2020. "An MPPT Control of a PMSG-Based WECS with Disturbance Compensation and Wind Speed Estimation," Energies, MDPI, vol. 13(23), pages 1-20, December.
    3. 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.
    4. Yuan Song & Taesu Jeon & Insu Paek & Bayasgalan Dugarjav, 2022. "Design and Validation of Pitch H-Infinity Controller for a Large Wind Turbine," Energies, MDPI, vol. 15(22), pages 1-15, November.
    5. Taesu Jeon & Dongmyoung Kim & Insu Paek, 2022. "Improvements to and Experimental Validation of PI Controllers Using a Reference Bias Control Algorithm for Wind Turbines," Energies, MDPI, vol. 15(21), pages 1-18, November.

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