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A coherent power-load optimization algorithm for wind farm-level yaw control considering wake effects via deep neural network

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Listed:
  • Wang, Yize
  • Liu, Zhenqing
  • Hu, Yilu
  • Bai, Guangpu

Abstract

The total power output of a wind farm can be improved via farm-level yaw control. However, previous studies have rarely considered wind turbine fatigue loads during power optimization; moreover, the complex wake turbulence effects between wind turbines have not been properly introduced. Consequently, this study proposes a coherent power-load optimization algorithm via yaw control. To implement this, the wind turbine structural dynamics and fatigue loads of the out-of-plane tower bottom moment under different wind speeds, turbulence intensities, yaw angles, and wake turbulence effects are calculated first. A novel deep neural network-based meta model is then proposed to predict fatigue loads. The network structure is well designed according to the data dimensions, ensuring good prediction performance, with an error of only 885 kN·m. Finally, the yaw angles of the wind turbines are optimized via the proposed algorithm, which is compared with a single power optimization algorithm. The numerical results indicate that, in contrast with the single-power optimization algorithm, the proposed coherent power-load optimization algorithm can achieve the same good power improvement of approximately 8 %; moreover, it can effectively reduce 85 % of the added fatigue loads caused by power optimization. The codes and attained networks are open for other researchers.

Suggested Citation

  • Wang, Yize & Liu, Zhenqing & Hu, Yilu & Bai, Guangpu, 2026. "A coherent power-load optimization algorithm for wind farm-level yaw control considering wake effects via deep neural network," Renewable Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:renene:v:257:y:2026:i:c:s0960148125023936
    DOI: 10.1016/j.renene.2025.124729
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