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Wake Management in Wind Farms: An Adaptive Control Approach

Author

Listed:
  • Harsh S. Dhiman

    (Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad 380026, India
    These authors contributed equally to this work.)

  • Dipankar Deb

    (Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad 380026, India
    These authors contributed equally to this work.)

  • Vlad Muresan

    (Department of Automation, Technical University of Cluj-Napoca, Cluj-Napoca 400114, Romania
    These authors contributed equally to this work.)

  • Valentina E. Balas

    (Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Bd Revolutiei 77, Arad 310130, Romania
    These authors contributed equally to this work.)

Abstract

Advanced wind measuring systems like Light Detection and Ranging (LiDAR) is useful for wake management in wind farms. However, due to uncertainty in estimating the parameters involved, adaptive control of wake center is needed for a wind farm layout. LiDAR is used to track the wake center trajectory so as to perform wake control simulations, and the estimated effective wind speed is used to model wind farms in the form of transfer functions. A wake management strategy is proposed for multi-wind turbine system where the effect of upstream turbines is modeled in form of effective wind speed deficit on a downstream wind turbine. The uncertainties in the wake center model are handled by an adaptive PI controller which steers wake center to desired value. Yaw angle of upstream wind turbines is varied in order to redirect the wake and several performance parameters such as effective wind speed, velocity deficit and effective turbulence are evaluated for an effective assessment of the approach. The major contributions of this manuscript include transfer function based methodology where the wake center is estimated and controlled using LiDAR simulations at the downwind turbine and are validated for a 2-turbine and 5-turbine wind farm layouts.

Suggested Citation

  • Harsh S. Dhiman & Dipankar Deb & Vlad Muresan & Valentina E. Balas, 2019. "Wake Management in Wind Farms: An Adaptive Control Approach," Energies, MDPI, vol. 12(7), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1247-:d:218955
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    References listed on IDEAS

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    Cited by:

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    2. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    3. Shibuya, Koichiro & Uchida, Takanori, 2023. "Wake asymmetry of yaw state wind turbines induced by interference with wind towers," Energy, Elsevier, vol. 280(C).
    4. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Wake management based life enhancement of battery energy storage system for hybrid wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    5. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.

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