This study discusses the characterization of farm blockage for an Onshore site. A test campaign was designed to measure the effects of farm induction upstream of a row of turbines. 5 LiDARs were dispersed on a site in North America. Measurement periods took place before and after erection of the turbines. A thorough characterization of the wind conditions is performed using the measurement from the pre-construction period. During nighttime, vertical profiles reveal the occurrence of low-level jet (LLJ) structure, and LiDAR-to-LiDAR horizontal variations in the measured wind speed are strong. Therefore, the analysis focuses on the daytime data only. Impact of farm blockage is quantified by analyzing variations of measured wind speed, relative to a LiDAR of reference, between the pre- and post-construction periods. These wind speed variations measured by the LiDARs, therefore, give insights on how the flow is distorted upstream of the row of turbines, but also within the inter-turbine space in the row. Additionally, the wind farm is simulated using a new CFD-based engineering model for blockage. Simulation results show very good agreement with the measurements, demonstrating the ability of the model to capture the underlying physics. The last part of the paper discusses the range of applicability of the test campaign results and proposes ways to further improve farm blockage characterization.

A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions.