Power production of the turbines at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at the Texas Tech University’s National Wind Institute Research Center was measured experimentally and simulated for neutral atmospheric boundary layer operating conditions. Two V27 wind turbines were aligned in series with the dominant wind direction, and the upwind turbine was yawed to investigate the impact of wake steering on the downwind turbine. Two conditions were investigated, including that of the leading turbine operating alone and both turbines operating in series. The field measurements include meteorological evaluation tower (MET) data and light detection and ranging (lidar) data. Computations were performed by coupling large eddy simulations (LES) in the three-dimensional, transient code Nalu-Wind with engineering actuator line models of the turbines from OpenFAST. The simulations consist of a coarse precursor without the turbines to set up an atmospheric boundary layer inflow followed by a simulation with refinement near the turbines. Good agreement between simulations and field data are shown. These results demonstrate that Nalu-Wind holds the promise for the prediction of wind plant power and loads for a range of yaw conditions.
Sandia National Laboratories and the National Renewable Energy Laboratory conducted a wake-steering field campaign at the Scaled Wind Farm Technology facility. The campaign included the use of two highly instrumented V27 wind turbines, an upstream met tower, and high-resolution wake measurements of the upstream wind turbine using a customized scanning lidar from the Technical University of Denmark (DTU). The present work investigates the impact of the upstream wake on the downstream turbine power and blade loads as the wake swept across the rotor in various waked conditions. The wake position was tracked using the DTU SpinnerLidar and synchronized to the met tower and turbine sensors. Fully and partially waked conditions reduced the power output and increased the fatigue loading on the downstream wind turbine. Partial wake impingement was found to result in a 10% increase in fatigue loading over the fully waked condition. Rotational sampling of the blade root bending moments revealed that the fatigue damage accrued during full turbine waking, was primarily caused by turbulence within the wake rather than velocity shear, while the partially waked turbine experienced a large 1-per revolution fatigue due to shear. The development of a power to fatigue load metric curve indicated the wake positions where shifting the wake has the most benefit for the waked turbine.
A method for measuring wake and aerodynamic properties of a wind turbine with reduced error based on simulated lidar measurements is proposed. A scanning lidar measures air velocity scalar projected onto its line of sight. However, line of sight is rarely parallel to the velocities of interest. The line of sight projection correction technique showed reduced axial velocity error for a simple wake model. Next, an analysis based on large-eddy simulations of a 27 m diameter wind turbine was used to more accurately assess the projection correction technique in a turbulent wake. During the simulation, flow behind the turbine is sampled with a nacelle mounted virtual lidar matching the scanning trajectory and sampling frequency of the DTU SpinnerLidar. The axial velocity, axial induction, freestream wind speed, thrust coefficient, and power coefficient are calculated from virtual lidar measurements using two different estimates of the flow: line of sight velocity without correction, and line of sight with projection correction. The flow field is assumed to be constant during one complete scan of the lidar field of view, and the average wind direction is assumed to be equal to the instantaneous wind direction at the lidar measurement location for the projection correction. Despite these assumptions, results indicate that all wake and aerodynamic quantity error is reduced significantly by using the projection correction technique; axial velocity error is reduced on average from 7.4% to 2.8%.
Herges, Thomas H.; Maniaci, D.C.; Naughton, B.T.; Mikkelsen, T.; Sjöholm, M.
High-resolution lidar wake measurements are part of an ongoing field campaign being conducted at the Scaled Wind Farm Technology facility by Sandia National Laboratories and the National Renewable Energy Laboratory using a customized scanning lidar from the Technical University of Denmark. One of the primary objectives is to collect experimental data to improve the predictive capability of wind plant computational models to represent the response of the turbine wake to varying inflow conditions and turbine operating states. The present work summarizes the experimental setup and illustrates several wake measurement example cases. The cases focus on demonstrating the impact of the atmospheric conditions on the wake shape and position, and exhibit a sample of the data that has been made public through the Department of Energy Atmosphere to Electrons Data Archive and Portal.