Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind-turbine wakes for different hub-height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met-tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub-height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near-wake velocity field, while it affects noticeably the far-wake evolution and recovery. A secondary effect on wake-recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake-generated turbulence fosters wake recovery. A semi-empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models. KEYWORDSLiDAR, wake, wind farm, wind turbine INTRODUCTIONThe recent worldwide outbreak of wind power production poses new challenges for wind farm designers seeking optimal layout and control strategies to maximize profitability of wind power plants. 1,2 A considerable factor for power losses and increased fatigue loads in large wind farms is connected with wake interactions, 3-6 which are affected by farm layout, turbine settings, site topography, and are highly variable with the static stability of the atmospheric boundary layer (ABL). 7-9 Furthermore, the increasing size of wind turbine rotors 10,11 exacerbates underperformance due to wake interactions as a consequence of the increased wake extent and, in turn, the longer downstream distance required for wake recovery.Continuous improvements in remote-sensing techniques, aiming to measure wind atmospheric turbulence, have been leveraged to achieve a deeper understanding of ABL flows 12-14 and to investigate the evolution of wakes produced by utility-scale wind turbines. [15][16][17][18] One of the first campaigns performed with light detection and ranging (LiDAR) systems with the goal of measuring wind-turbine wakes took place at a site near the coast of the northern part of Germany to probe reduction of the wind speed at certain distances downstream of a wind turbine rotor. 19Since then, a wide range of scann...

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This paper discusses the idea of vertical wake deflection for offshore wind turbines using active ballast control. First, the concept of active ballast control for changing the attitude of an offshore wind energy semi-submersible platform is presented and briefly explored. Next, the influence of vertical wake deflection on the wake behavior and power production of a cluster of two wind turbines is studied through wind tunnel experiments and LES simulations. Results show that vertical deflection changes substantially the wake characteristics and affects the inflow at a downstream wind turbine in terms of available kinetic energy and turbulence intensity. For the most favorable case considered here, the cluster power was increased by approximately 8%.