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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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    2. Bao, Yuanyi & Ling, Ziyan & Zhao, Zhenzhou & Jin, Heping & Shen, Yangwu & Xiao, Yu & Xiang, Xingchen & Lu, Yanlie & Wang, Yingke & Liu, Ting, 2026. "A novel three-dimensional dual-cosine wake model for wind turbine full wake predictions," Renewable Energy, Elsevier, vol. 259(C).
    3. Wenfeng, Li & Zhenzhou, Zhao & Yige, Liu & Huiwen, Liu & Shangshang, Wei & Ali, Kashif & Guodan, Dong & Yan, Liu & Yuanzhuo, Ma, 2025. "Investigation of dynamic wake model of a floating offshore wind turbine under heave, surge and pitch motion," Renewable Energy, Elsevier, vol. 254(C).
    4. Sang, Yiyan & Dai, Zhuping & Ren, Yaxing & Wu, Qigang & Wang, Yufei & Xue, Hua, 2025. "NESO-based nonlinear proportional-integral pitch control of the TLP-based floating offshore wind turbine," Energy, Elsevier, vol. 333(C).
    5. Kong, Luming & Xiao, Feng & Yang, Haobo & Fang, Fang & Qin, Shiyao, 2025. "Distributed optimal control of wind farms with deloading operation for load sharing," Energy, Elsevier, vol. 339(C).
    6. Bin Shahadat, Muhammad Rubayat & Doranehgard, Mohammad Hossein & Cai, Weibing & Li, Zheng, 2025. "Large eddy simulation of wind farm performance in horizontally and vertically staggered layouts," Energy, Elsevier, vol. 322(C).
    7. Wang, Yize & Liu, Zhenqing & Yu, Zhongze, 2025. "An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction," Energy, Elsevier, vol. 340(C).
    8. Wang, Yuhong & Cao, Tian & Gao, Shilin & Chen, Ying & Liao, Jianquan & Zheng, Zongsheng & Zhai, Bingjie, 2026. "A hybrid mechanism-based and data-driven dynamic equivalence method for PMSG wind farms considering wake effect and fault severity," Renewable Energy, Elsevier, vol. 258(C).
    9. Yao, Qi & Liang, Zemin & Guan, Yuanpeng & Ke, Yiming & Hu, Yang & Fang, Fang & Liu, Jizhen, 2026. "Wake model-free active power control for wind-storage coordination using a state-decoupled MATD3 algorithm," Energy, Elsevier, vol. 347(C).
    10. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lu, Hongkun & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu & Zhao, Wensheng, 2025. "Coupled effects of 3D wake expansion and terrain anisotropy on downstream wind turbine power performance and fatigue load," Energy, Elsevier, vol. 334(C).
    11. Zhang, Teng & Xu, Xiaosen & Wang, Shuaishuai & Xing, Yihan & Dou, Peng & Ji, Renwei & Yang, Puyi, 2025. "Investigation of wake steering control effects on the dynamic responses of 15 MW semi-submersible floating wind farms," Renewable Energy, Elsevier, vol. 254(C).

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