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Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines

Author

Listed:
  • Cao, Lichao
  • Ge, Mingwei
  • Gao, Xiaoxia
  • Du, Bowen
  • Li, Baoliang
  • Huang, Zhi
  • Liu, Yongqian

Abstract

High turbulence intensity induced by the wake effect in wind farms is of great significance to the fatigue life of wind turbines. However, previous wind farm layout optimization (WFLO) only considers the shading effect between wind turbines. To fill this critical gap, we propose a novel multi-objective WFLO (MO-WFLO) framework considering both the power generation and the distribution of streamwise turbulence intensity in the wind farm. In this framework, a newly developed turbulence intensity model of turbine wake is used to calculate the turbulence intensity distribution, and a two-dimensional Gaussian wake model is adopted to calculate the wake losses of the wind farm, in which the wake expansion rate of turbine wake is determined accounting for the turbulence intensity of the turbine’s inflow. The nondomination sorting genetic algorithm with elite strategy (NSGA-II) is resorted to maximize the total power generation and to minimize the comprehensive streamwise turbulence intensity of the turbines’ inflow. The results show that under the same annual energy production, our proposed framework can effectively reduce the maximum turbulence intensity level of the turbines with high fatigue loads, and shorten the cumulative operation time of the wind turbines in high turbulence conditions in the wind farm. By applying the proposed framework to a realistic wind farm, a new layout can be obtained with increased total power by 0.8% and a reduced maximum comprehensive streamwise turbulence intensity of the turbines by 8.1% compared with the original layout.

Suggested Citation

  • Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009060
    DOI: 10.1016/j.apenergy.2022.119599
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    References listed on IDEAS

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