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Adaptive State Feedback—Theory and Application for Wind Turbine Control

Author

Listed:
  • Kaman Thapa Magar

    (Aerospace Mechanics Division, University of Dayton Research Institute, 300 College Park, Dayton, OH 45469, USA)

  • Mark Balas

    (Mechanical, Aerospace and Biomedical Engineering, University of Tennessee Space Institute, Tullahoma, TN 37388, USA)

  • Susan Frost

    (NASA Ames Research Center, Mofett Field, CA 95034, USA)

  • Nailu Li

    (School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China)

Abstract

A class of adaptive disturbance tracking controllers (ADTCs) is augmented with disturbance and state estimation and adaptive state feedback, in which a controller and estimator, which are designed on the basis of a lower-order model, are used to control a higher-order nonlinear plant. The ADTC requires that the plant be almost strict positive real (ASPR) to ensure stability. In this paper, we show that the ASPR property of a plant is retained with the addition of disturbance and state estimation and state feedback, thereby ensuring the stability of the augmented system. The proposed adaptive controller with augmentation is presented in the context of maximum power extraction from a wind turbine in a low-wind-speed operation region. A simulation and comparative study on the National Renewable Energy Laboratory’s (NREL’s) 5 MW nonlinear wind turbine model with an existing baseline Proportional-Integral-Derivative(PID) controller shows that the proposed controller is more effective than the existing baseline PID controller.

Suggested Citation

  • Kaman Thapa Magar & Mark Balas & Susan Frost & Nailu Li, 2017. "Adaptive State Feedback—Theory and Application for Wind Turbine Control," Energies, MDPI, vol. 10(12), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2145-:d:123098
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    References listed on IDEAS

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    1. 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.
    2. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    3. Jaramillo-Lopez, Fernando & Kenne, Godpromesse & Lamnabhi-Lagarrigue, Francoise, 2016. "A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction," Renewable Energy, Elsevier, vol. 86(C), pages 38-48.
    4. 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.
    5. Narayana, Mahinsasa & Sunderland, Keith M. & Putrus, Ghanim & Conlon, Michael F., 2017. "Adaptive linear prediction for optimal control of wind turbines," Renewable Energy, Elsevier, vol. 113(C), pages 895-906.
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    Cited by:

    1. Baiomy, Nehal & Kikuuwe, Ryo, 2020. "An amplitude- and rate-saturated collective pitch controller for wind turbine systems," Renewable Energy, Elsevier, vol. 158(C), pages 400-409.

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