Lidar Measurements of Wakes Generated by Utility-scale Wind Turbines and Data-driven Optimization of Wind-turbine Wake Models




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Current utility-scale wind turbines are the largest rotating turbo-machines ever built in the engineering history operating in a heterogeneous and dynamic environment, namely the atmospheric boundary layer. Wind features, such as Reynolds number of the order of several millions, wind shear and veer, daily cycle of atmospheric stability and wind turbulence intensity, topographyinduced wind distortions and wake interactions, among others, make theoretical and numerical predictions highly challenging to achieve accuracy in predictions of the turbine performance and wakes, which is not always sufficient for wind energy applications. These challenges are the motivation to investigate wind turbine operations and wakes through wind LiDAR measurements coupled with meteorological and SCADA data. Two main field campaigns have been performed: one for a wind farm deployed over a relatively flat terrain in Texas and another for a wind power plant installed on a complex terrain located in Colorado. Innovative LiDAR scanning strategies have been developed in order to collect accurate wind data around wind turbines and to probe the intra-wind farm wind field characterized by significant wake interactions. Transformative methodologies have been developed to analyze LiDAR and wind turbine data, such as the wake-cluster analysis and estimation of wake-wind statistics through the Barnes scheme. These detailed investigations have enabled characterizations of wake evolution for broad ranges of wind conditions and operative conditions of the wind turbines. Finally, the collected experimental data have been leveraged to perform diagnostic studies of wind farm performance and to carry-out data-driven tuning of existing wind-turbine wake models in order to provide optimized engineering tools for the wind energy community.



Optical radar, Remote sensing, Wind turbines