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Adaptive envelope protection control of wind turbines under varying operational conditions

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  • Sahin, Mustafa
  • Yavrucuk, Ilkay

Abstract

This study introduces a new Envelope Protection System (EPS) algorithm for wind turbines. The algorithm is adaptive to turbine-changing operational conditions and can effectively reduce turbine excessive/ultimate loads. Through an adaptive neural network, the proposed algorithm continuously monitors instantaneous wind and turbine states. Simultaneously, it predicts the near future response of the turbine load and detects its future crossing with a predefined safe envelope limit by comparing the actual wind speed to a theoretically estimated wind speed. When required, a protection action is applied based on the comparison to keep the turbine load response within the safe limit. In this paper, the thrust force is used as the critical load and is chosen as the limit parameter. Simulations are carried out using the MS (Mustafa Sahin) Bladed Wind Turbine Simulation Model for the National Renewable Energy Laboratory (NREL) 5 MW turbine under normal turbulent winds with different mean values. Simulations show that the EPS algorithm adapts to varying operational conditions such as changes in turbine operating point in the below rated, transition, and above rated regions, as well as rotor blade icing and successfully reduces the excessive thrust forces. Performance analyses indicate that, for keeping the thrust force within the limit, the proposed EPS algorithm reduces the thrust force by 98.89%, 98.43%, 99.26% relative to standard baseline controls in the aforementioned regions, respectively and by 99.61% under blade icing. Also, the mean value and the fluctuations of thrust force are reduced up to 5.52% and 68.7%, respectively. Depending on the operating region, the mean power decreases up to 2.07% or increases up to 1.21%, while power fluctuations decrease up to 30.97%.

Suggested Citation

  • Sahin, Mustafa & Yavrucuk, Ilkay, 2022. "Adaptive envelope protection control of wind turbines under varying operational conditions," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222004479
    DOI: 10.1016/j.energy.2022.123544
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    References listed on IDEAS

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    Cited by:

    1. Chen, Peng & Han, Dezhi, 2023. "Reward adaptive wind power tracking control based on deep deterministic policy gradient," Applied Energy, Elsevier, vol. 348(C).

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