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Alternative operational strategies for wind turbines in cold climates

Author

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  • Stoyanov, D.B.
  • Nixon, J.D.

Abstract

Around a quarter of the global wind energy capacity is operating in cold climates, where ice accretion can damage wind turbines, cause safety concerns and reduce power output. In this paper, alternative operational strategies to reduce ice build-up and increase power output are studied. The alternative strategies are achieved by making tip-speed ratio (TSR) modifications both during and after an icing event. To compare different TSR strategies, the concept of an energy payback time is outlined, which is used to determine when an alternative strategy outperforms a turbine's normal design strategy. The method is demonstrated using the NREL 5 MW reference wind turbine for twelve different icing conditions, encompassing different temperatures, wind speeds, droplet diameters and liquid water contents. The results indicate that for short and severe icing events, an alternative TSR strategy will start producing more energy than a conventional design strategy within 0.5–2.5 h after icing and decrease ice accumulation by approximately 25–30% per blade. The method presented in this study will enable more effective operational control strategies to be deployed for minimising ice-induced power losses and ice accretion at wind farms located in cold climates.

Suggested Citation

  • Stoyanov, D.B. & Nixon, J.D., 2020. "Alternative operational strategies for wind turbines in cold climates," Renewable Energy, Elsevier, vol. 145(C), pages 2694-2706.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:2694-2706
    DOI: 10.1016/j.renene.2019.08.023
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    Cited by:

    1. Stoyanov, D.B. & Nixon, J.D. & Sarlak, H., 2021. "Analysis of derating and anti-icing strategies for wind turbines in cold climates," Applied Energy, Elsevier, vol. 288(C).
    2. Hacıefendioğlu, Kemal & Başağa, Hasan Basri & Yavuz, Zafer & Karimi, Mohammad Tordi, 2022. "Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method," Renewable Energy, Elsevier, vol. 182(C), pages 1-16.
    3. Gao, Linyue & Tao, Tao & Liu, Yongqian & Hu, Hui, 2021. "A field study of ice accretion and its effects on the power production of utility-scale wind turbines," Renewable Energy, Elsevier, vol. 167(C), pages 917-928.
    4. Yong Ma & Shan Ai & Lele Yang & Aiming Zhang & Sen Liu & Binghao Zhou, 2020. "Hydrodynamic Performance of a Pitching Float Wave Energy Converter," Energies, MDPI, vol. 13(7), pages 1-27, April.

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