High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm

Characterizing wind farm flow fields at high temporal and spatial resolutions is critical prerequisite for the optimal design and operation of utility-scale wind farms and for reducing the levelized cost of energy. However, due to the large disparity of underlying scales, measurements or simulations alone cannot provide high resolution wind fields, which are informed by and account for the effect of both large scale (i.e. hour, day, month and year) and small scale (i.e. second and minute) site-specific variations in the atmosphere. We explore the feasibility of integrating field measurements and high-fidelity large-eddy simulation (LES) to characterize the wind field in a utility-scale wind farm while accounting for flow phenomena across multiple temporal scales. Specifically, we employ field measurements to characterize the monthly wind speed and wind direction distributions and investigate the wind characteristics in turbine wakes. It was found that the probability density function (PDF) of the wind speed in turbine wakes can be reasonably represented using the Weibull distribution but with shape factors smaller than those not in the wake. LES of the wind farm under statistically steady inflow is subsequently carried out for one wind direction. The LES predictions are compared with the measured data conditionally averaged based on the wind speed, wind direction and the root-mean-square of wind speed fluctuations over time intervals of 30 min. Good agreement is obtained for both mean wind speed and turbulence intensity. The present work shows the possibility of integrating field measurements and high-fidelity simulations for improved characterization of the site-specific wind fields in utility-scale wind farms.

Automated summary

The research demonstrates the potential of integrating field measurements and high-fidelity large-eddy simulation (LES) for improved characterization of the wind fields in utility-scale wind farms. The probability density function (PDF) of wind speeds in turbine wakes can be reasonably represented using the Weibull distribution. Comparisons between LES predictions and measured data show good agreement for mean wind speed and turbulence intensity.