Farm blockage model validation using pre and post construction LiDAR measurements

Abstract

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.

On the Accuracy of Predicting Wind-Farm Blockage

“…The previous section clearly established that RANS simulations and wind-tunnel measurements predict wind-farm blockage consistently (in-line with comparisons between RANS and full-scale measurements [11,15,22]); yet, it also demonstrated that wind-tunnel confinement reduces blockage, whilst also complicating its prediction. However, for industrial applications, the accurate prediction of blockage in an unconfined setting counts.…”

“…Whereas a 1% error is certainly acceptable in wake-loss prediction (usually on the order of 10%-35%), it is definitely not in blockage, as the error would be of similar order. In general terms, RANS methods are able to characterise blockage accurately [11,15,28,29], as farm blockage is coupled to the upwind pressure gradient given by the mean flow [24]. Large-Eddy Simulations (LES) capture unsteadiness, a wide range of turbulence scales and energy fluxes, thereby providing a more detailed description of the instantaneous flow field than RANS and catching the interaction between atmosphere and wind farm, especially in buoyancy-driven flows [25,26].…”

Numerical simulations for a parametric study of blockage effect on offshore wind farms

“…For a low C T , there was little to no impact on the velocity upstream of the wind farm, while for larger C T , the velocity was reduced by 4.5%. Jaquet et al 10 performed a study where the wind speed was measured pre‐ and post‐installation of the wind farm. The results show that the upstream wind speed decreased by up to 3% after the wind farm had been installed.…”

Characterization of wind turbine flow through nacelle-mounted lidars: a review

“…Several scanning technologies and strategies have been adopted to probe the atmospheric boundary layer for wind energy applications. Profiling lidars have been deployed for freestream resource assessment (Gottschall et al, 2012;Optis et al, 2021;Sharma et al, 2021), wake characterization (Heisel et al, 2018) and plant induction quantification (Jacquet et al, 2022;Letizia et al, 2022). Scanning lidars, i.e., those that feature the ability to direct the laser beam in arbitrary directions (among other abilties), offer additional flexibility and permit a more targeted characterization of the wind plant flow.…”