Articles | Volume 5, issue 4
Wind Energ. Sci., 5, 1601–1622, 2020
https://doi.org/10.5194/wes-5-1601-2020
Wind Energ. Sci., 5, 1601–1622, 2020
https://doi.org/10.5194/wes-5-1601-2020

Research article 19 Nov 2020

Research article | 19 Nov 2020

Optimal tuning of engineering wake models through lidar measurements

Lu Zhan et al.

Data sets

LiDAR Cluster Statistic of Wind Turbine Wakes G. V. Iungo https://doi.org/10.5281/zenodo.3604444

Model code and software

Matlab function for optimal tuning of wake engineering models L. Zhan, S. Letizia, and G. V. Iungo https://www.utdallas.edu/windflux/

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Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.