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Validations of three-dimensional wake models with the wind field measurements in complex terrain

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  • Sun, Haiying
  • Gao, Xiaoxia
  • Yang, Hongxing

Abstract

This paper aims to validate the analytical three-dimensional wake models by the wind data measured at a complex-terrain hilly wind farm in China. The wake models take into account the variation of wind speed in the vertical direction. The profiles of wind speeds were scanned by two moveable lidars. Two scanning modes were adopted to obtain the profiles of wind speed in vertical and horizontal directions, respectively. When validating the wake model for a single wind turbine in the vertical direction, the model can predict the wind speeds with acceptable accuracy, especially at positions beyond 10D downstream distance or above 100 m height. Some large errors were found at positions less than 40 m height. Wind speeds in two symmetrical side sections showed different distributions at the same downwind positions. When validating the wake model for multiple wind turbines in horizontal direction, the model also had a reliable accuracy at the far wake positions and near the inflow measuring site, but it cannot predict the wind deficits before an operating wind turbine and was not accurate in some complex-terrain situations. Suggestions and factors were given to be considered for improving the wake models in the future.

Suggested Citation

  • Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2019. "Validations of three-dimensional wake models with the wind field measurements in complex terrain," Energy, Elsevier, vol. 189(C).
  • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319085
    DOI: 10.1016/j.energy.2019.116213
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    References listed on IDEAS

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    1. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
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    6. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.
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    Cited by:

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    3. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    4. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "Experimental study on wind speeds in a complex-terrain wind farm and analysis of wake effects," Applied Energy, Elsevier, vol. 272(C).
    5. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    6. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    7. Chen, Zhenyu & Lin, Zhongwei & Zhai, Xiaoya & Liu, Jizhen, 2022. "Dynamic wind turbine wake reconstruction: A Koopman-linear flow estimator," Energy, Elsevier, vol. 238(PB).
    8. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    9. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    10. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    11. Pacheco de Sá Sarmiento, Franciene Izis & Goes Oliveira, Jorge Luiz & Passos, Júlio César, 2022. "Impact of atmospheric stability, wake effect and topography on power production at complex-terrain wind farm," Energy, Elsevier, vol. 239(PC).
    12. Dhoot, Aditya & Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2021. "Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability," Energy, Elsevier, vol. 223(C).
    13. Sun, Chong & Tian, Tian & Zhu, Xiaocheng & Hua, Ouyang & Du, Zhaohui, 2021. "Investigation of the near wake of a horizontal-axis wind turbine model by dynamic mode decomposition," Energy, Elsevier, vol. 227(C).
    14. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
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