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Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine

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  • Jeon, Sanghyeon
  • Kim, Bumsuk
  • Huh, Jongchul

Abstract

Wind turbines installed in a wind farm can be affected by wake from neighboring wind turbines, which reduces power production and shortens turbine life due to mechanical fatigue. Therefore, careful consideration of the wake effects is necessary before establishing an optimum layout design for wind farms. Until now, various numerical models have been developed and applied to evaluate the wake effects, but comparison among (and verification of) the measurement results conducted at numerous wind farms have been few and insufficient. To verify the prediction accuracy of engineering wake models, namely, eddy viscosity, Larsen, Jensen, and Frandsen models, which are widely used in wind energy business, the current study presents the results of the comparative analysis of the values measured at a commercially operated onshore wind farm. The results demonstrate that the Jensen model is the best model in predicting the wake-centered velocity deficit under a specific wind-speed condition, and the eddy viscosity and Larsen models are relatively accurate in predicting the width of the wake and its profile. In conclusion, we need to carefully apply a model for wake-effect assessment based on the spacing between wind turbines because the prediction accuracy of the wake models varies with the downstream distance condition.

Suggested Citation

  • Jeon, Sanghyeon & Kim, Bumsuk & Huh, Jongchul, 2015. "Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine," Energy, Elsevier, vol. 93(P2), pages 1769-1777.
  • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1769-1777
    DOI: 10.1016/j.energy.2015.09.086
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    Cited by:

    1. Khan, Mehtab Ahmad & Javed, Adeel & Shakir, Sehar & Syed, Abdul Haseeb, 2021. "Optimization of a wind farm by coupled actuator disk and mesoscale models to mitigate neighboring wind farm wake interference from repowering perspective," Applied Energy, Elsevier, vol. 298(C).
    2. Tian, Linlin & Song, Yilei & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling & Wang, Tongguang, 2020. "Effects of turbulence modelling in AD/RANS simulations of single wind & tidal turbine wakes and double wake interactions," Energy, Elsevier, vol. 208(C).
    3. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
    4. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
    6. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    7. Shin, Dongheon & Ko, Kyungnam, 2022. "Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance," Energy, Elsevier, vol. 243(C).
    8. Kamada, Yasunari & Li, Qing'an & Maeda, Takao & Yamada, Keisuke, 2019. "Wind tunnel experimental investigation of flow field around two-dimensional single hill models," Renewable Energy, Elsevier, vol. 136(C), pages 1107-1118.
    9. Ingrid Neunaber & Michael Hölling & Martin Obligado, 2022. "Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development," Energies, MDPI, vol. 15(22), pages 1-15, November.
    10. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    11. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    12. Hyungyu Kim & Kwansu Kim & Carlo Luigi Bottasso & Filippo Campagnolo & Insu Paek, 2018. "Wind Turbine Wake Characterization for Improvement of the Ainslie Eddy Viscosity Wake Model," Energies, MDPI, vol. 11(10), pages 1-19, October.
    13. Syed, Abdul Haseeb & Javed, Adeel & Asim Feroz, Raja M. & Calhoun, Ronald, 2020. "Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations," Applied Energy, Elsevier, vol. 268(C).
    14. Zhenzhou Shao & Ying Wu & Li Li & Shuang Han & Yongqian Liu, 2019. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes," Energies, MDPI, vol. 12(4), pages 1-14, February.

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