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A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain

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  • Qian, Guo-Wei
  • Ishihara, Takeshi

Abstract

This study proposes a novel probabilistic power curve model for wind turbine and combines it with a hybrid wind farm model to quantify the accuracy and uncertainty of power prediction of wind farm over complex terrain with low computational cost. The proposed probabilistic power curve model for an active stall-regulated turbine is expressed by the beta distribution to estimate the uncertainty of power output at a certain wind speed. The predicted mean value and standard deviation of power output by the proposed model show favorable agreement with the measurement, while the conventional deterministic model cannot estimate the uncertainty of power output from wind turbines at all. The hybrid wind farm flow model is then presented, in which the effects of local terrain and surface roughness on the wind speed, wind direction and turbulence intensity are taken into account by the CFD simulation, and the wind turbine wakes are represented by an advanced wake model. The predicted wind speed and turbulence intensity show good agreement with those measured in a wind farm over complex terrain in the north of Japan. Finally, the proposed probabilistic power curve model is combined with the hybrid farm flow model to estimate the mean value and standard deviation of wind farm power production and is validated by the field measurement. The weighted mean absolute percentage error in mean value is reduced from 18.1% to 7.2% with consideration of wake effects and that in standard deviation is reduced from 100% to 15.6% by using the proposed probabilistic power curve model.

Suggested Citation

  • Qian, Guo-Wei & Ishihara, Takeshi, 2022. "A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain," Energy, Elsevier, vol. 261(PA).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222020631
    DOI: 10.1016/j.energy.2022.125171
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    References listed on IDEAS

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