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The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine

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  • Yang, Haoze
  • Ge, Mingwei
  • Gu, Bo
  • Du, Bowen
  • Liu, Yongqian

Abstract

Swell is a common type of sea wave, however, significant knowledge gaps exist regarding how the swells influence the marine atmospheric boundary layer and the operation of wind turbines. In this study, the effects of downwind and upwind swells with three different wave ages (|c/u∗| ≈ 30, 60, and 90) are investigated using large-eddy simulation. We find that downwind swells lead to a shear layer with accelerated wind velocity and decreased turbulence intensity. In comparing with that without swells, the hub-height wind velocity above swells with c/u∗ ≈ 30, 60, and 90 increases by 3.7%, 16.1%, and 23.3%, respectively, while the turbulence kinetic energy decreases over 40% and varies non-monotonously with the wave age. In contrast, upwind swells induce a substantially decreased wind profile with enhanced turbulence. For turbines operating above downwind or upwind swells with |c/u∗| ≈ 90, the power output is nearly enhanced by 100% or reduced by 50%, and a characteristic frequency of the turbines' power fluctuations exists identical to the swell's intrinsic frequency. The turbine wake is affected by both the swell-changed wind velocity and turbulence intensity. Above the downwind (upwind) swell, the wake shows a slower (higher) recovery and weaker (stronger) meandering.

Suggested Citation

  • Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s0360544222001037
    DOI: 10.1016/j.energy.2022.123200
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    References listed on IDEAS

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    1. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    2. Amin Allah, Veisi & Shafiei Mayam, Mohammad Hossein, 2017. "Large Eddy Simulation of flow around a single and two in-line horizontal-axis wind turbines," Energy, Elsevier, vol. 121(C), pages 533-544.
    3. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    4. Ge, Mingwei & Zhang, Shuaibin & Meng, Hang & Ma, Hongliang, 2020. "Study on interaction between the wind-turbine wake and the urban district model by large eddy simulation," Renewable Energy, Elsevier, vol. 157(C), pages 941-950.
    5. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    6. Zhang, Shuaibin & Du, Bowen & Ge, Mingwei & Zuo, Yingtao, 2022. "Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks," Renewable Energy, Elsevier, vol. 183(C), pages 708-718.
    7. Yang, Di & Meneveau, Charles & Shen, Lian, 2014. "Effect of downwind swells on offshore wind energy harvesting – A large-eddy simulation study," Renewable Energy, Elsevier, vol. 70(C), pages 11-23.
    8. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    9. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    10. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    11. Lopez, Daniel & Kuo, Jim & Li, Ni, 2019. "A novel wake model for yawed wind turbines," Energy, Elsevier, vol. 178(C), pages 158-167.
    12. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
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    1. Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).

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