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Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering

Author

Listed:
  • Liu, Yige
  • Zhao, Zhenzhou
  • Liu, Yan
  • Liu, Huiwen
  • Wei, Shangshang
  • Ma, Yuanzhuo
  • Ling, Ziyan
  • Luo, Qiao

Abstract

The wake of the wind turbines leads to power losses in wind farms. The active wake control (AWC) method reduces the wake effect and increases the power output. This paper systematically studies the combined wake control (CWC) method, which simultaneously controls the yaw angles, rotor speeds, and pitch angles of turbines to enhance the power output of wind farms. The prediction of the spatial distribution of the wake velocity under different operating states of wind turbines is a key factor in AWC. This study proposes a novel three-dimensional wake model for yawed wind turbine, considering wind shear and secondary steering. This model is validated through comparisons with large eddy simulation. A database for the aerodynamic performances of the NREL 5 MW wind turbine is then established based on OpenFAST. The whale optimization algorithm is used to optimize the wake effects between wind turbines in order to maximize the overall power output of a wind farm consisting of five aligned NREL 5 MW turbines. The obtained results indicate that CWC effectively improves the power output of the wind farm, with better performance than the axial induce factor control and wake redirection control by leveraging more turbine degrees of freedom. Moreover, secondary steering effects allow some downstream turbines need only slight active yaw adjustments to deflect wake considerably. Variations in inflow wind speed, ambient turbulence intensity, and turbine streamwise spacing all influence CWC. Overall, the more serious the wake effects within the wind farm, the greater the power improvement offered by CWC.

Suggested Citation

  • Liu, Yige & Zhao, Zhenzhou & Liu, Yan & Liu, Huiwen & Wei, Shangshang & Ma, Yuanzhuo & Ling, Ziyan & Luo, Qiao, 2024. "Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224026744
    DOI: 10.1016/j.energy.2024.132900
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    References listed on IDEAS

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    1. van Zalk, John & Behrens, Paul, 2018. "The spatial extent of renewable and non-renewable power generation: A review and meta-analysis of power densities and their application in the U.S," Energy Policy, Elsevier, vol. 123(C), pages 83-91.
    2. De-Zhi Wei & Ni-Na Wang & De-Cheng Wan, 2021. "Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model," Energies, MDPI, vol. 14(15), pages 1-26, July.
    3. 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).
    4. 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.
    5. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2015. "Maximizing the overall production of wind farms by setting the individual operating point of wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 219-229.
    6. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    7. Sun, Haiying & Yang, Hongxing, 2018. "Study on an innovative three-dimensional wind turbine wake model," Applied Energy, Elsevier, vol. 226(C), pages 483-493.
    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. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Yang, Xiang I.A., 2019. "A two-dimensional Jensen model with a Gaussian-shaped velocity deficit," Renewable Energy, Elsevier, vol. 141(C), pages 46-56.
    10. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    11. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    12. Zong, Haohua & Porté-Agel, Fernando, 2021. "Experimental investigation and analytical modelling of active yaw control for wind farm power optimization," Renewable Energy, Elsevier, vol. 170(C), pages 1228-1244.
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