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Prediction of multi-wake problems using an improved Jensen wake model

Author

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  • Tian, Linlin
  • Zhu, Weijun
  • Shen, Wenzhong
  • Song, Yilei
  • Zhao, Ning

Abstract

The improved analytical wake model named as 2D_k Jensen model (which was proposed to overcome some shortcomes in the classical Jensen wake model) is applied and validated in this work for wind turbine multi-wake predictions. Different from the original Jensen model, this newly developed 2D_k Jensen model uses a cosine shape instead of the top-hat shape for the velocity deficit in the wake, and the wake decay rate as a variable that is related to the ambient turbulence as well as the rotor generated turbulence. Coupled with four different multi-wake combination models, the 2D_k Jensen model is assessed through (1) simulating two wakes interaction under full wake and partial wake conditions and (2) predicting the power production in the Horns Rev wind farm for different wake sectors around two different wind directions. Through comparisons with field measurements, results from Large Eddy Simulations (LES) as well as results from other commercial codes, it is found that the predictions obtained with the 2D_k Jensen model exhibit good to excellent agreements with experimental and LES data.

Suggested Citation

  • Tian, Linlin & Zhu, Weijun & Shen, Wenzhong & Song, Yilei & Zhao, Ning, 2017. "Prediction of multi-wake problems using an improved Jensen wake model," Renewable Energy, Elsevier, vol. 102(PB), pages 457-469.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pb:p:457-469
    DOI: 10.1016/j.renene.2016.10.065
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    1. 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.
    2. 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.
    3. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
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    3. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
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    13. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
    14. 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).
    15. Ling, Ziyan & Zhao, Zhenzhou & Liu, Yige & Liu, Huiwen & Ali, Kashif & Liu, Yan & Wen, Yifan & Wang, Dingding & Li, Shijun & Su, Chunhao, 2024. "Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model," Renewable Energy, Elsevier, vol. 227(C).
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