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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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    1. Lin, Yonggang & Tu, Le & Liu, Hongwei & Li, Wei, 2016. "Fault analysis of wind turbines in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 482-490.
    2. Njiri, Jackson G. & Beganovic, Nejra & Do, Manh H. & Söffker, Dirk, 2019. "Consideration of lifetime and fatigue load in wind turbine control," Renewable Energy, Elsevier, vol. 131(C), pages 818-828.
    3. Abhineet Gupta & Mario A. Rotea & Mayank Chetan & Mohammad S. Sakib & D. Todd Griffith, 2021. "A Methodology for Robust Load Reduction in Wind Turbine Blades Using Flow Control Devices," Energies, MDPI, vol. 14(12), pages 1-29, June.
    4. Zhang, Mingming & Yang, Honglei & Xu, Jianzhong, 2017. "Numerical investigation of azimuth dependent smart rotor control on a large-scale offshore wind turbine," Renewable Energy, Elsevier, vol. 105(C), pages 248-256.
    5. Zhang, Mingming & Li, Xin & Tong, Jingxin & Xu, Jianzhong, 2020. "Load control of floating wind turbine on a Tension-Leg-Platform subject to extreme wind condition," Renewable Energy, Elsevier, vol. 151(C), pages 993-1007.
    6. Camblong, H. & Nourdine, S. & Vechiu, I. & Tapia, G., 2012. "Control of wind turbines for fatigue loads reduction and contribution to the grid primary frequency regulation," Energy, Elsevier, vol. 48(1), pages 284-291.
    7. Petrović, Vlaho & Bottasso, Carlo L., 2017. "Wind turbine envelope protection control over the full wind speed range," Renewable Energy, Elsevier, vol. 111(C), pages 836-848.
    8. Bofeng Xu & Yue Yuan & Haoming Liu & Peng Jiang & Ziqi Gao & Xiang Shen & Xin Cai, 2020. "A Pitch Angle Controller Based on Novel Fuzzy-PI Control for Wind Turbine Load Reduction," Energies, MDPI, vol. 13(22), pages 1-16, November.
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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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