LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Abstract

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...

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

“…4.2 is carried out using statistics collected under near-neutral conditions at Alpha Ventus, i.e., low atmospheric turbulence. Nevertheless, atmospheric turbulence conditions has a strong impact on the wake development (Kumer et al, 2017;Zhan et al, 2020), and wind turbine loads Kretschmer et al, 2018). Further, the lidar measuring characteristics can impact the accuracy of reconstructed fields (Lundquist et al, 2015), thus that of load predictions.…”

LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part II: Applications to synthetic and real LiDAR data of wind turbine wakes

“…4. The streamwise component is estimated from the line-of-sight velocity through an equivalent velocity approach (Zhan et al, 2019), then mean velocity and turbulence intensity (i.e. the ratio between standard deviation and mean) are reconstructed through the LiSBOA.…”

“…Using this approach, Iungo and Porté-Agel (2014) detected a significant dependence of the wake recovery rate on atmospheric stability based on time-averaged volumetric LiDAR scans. The same concept was expanded by other authors using ensemble statistics (Machefaux et al, 2016;Carbajo Fuertes et al, 2018;Zhan et al, 2019Zhan et al, , 2020. Kumer et al (2015) carried out a comparison between instantaneous, 10 minutes and daily-averaged velocity and turbulence intensity fields around utility-scale wind turbines, highlighting the presence of persistent turbulent wakes.…”

“…A deeper understanding on the physics of turbine wakes was achieved by calculating temporal (Trujillo et al, 2011;Iungo et al, 2013b;Iungo and Porté-Agel, 2014;Kumer et al, 2015;Machefaux et al, 2016;Van Dooren et al, 2016) or conditional (Aubrun et al, 2016;Machefaux et al, 2016;Garcia et al, 2017;Bromm et al, 2018;Iungo et al, 2018;Zhan et al, 2019Zhan et al, , 2020) statistics of the velocity collected through LiDAR scans performed at different times. Using this approach, Iungo and Porté-Agel (2014) detected a significant dependence of the wake recovery rate on atmospheric stability based on time-averaged volumetric LiDAR scans.…”

“…larger than 50%. Zhan et al (2019) used clustered data of wake velocity fields to retrieve a proxy for the standard deviation of wind speed in the wake of utility-scale turbines. These authors reported significant variability in the wake turbulent statistics depending on the atmospheric stability regime and operative conditions of the wind turbines.…”