Paula Doubrawa

Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.

Contributions of the Stochastic Shape Wake Model to Predictions of Aerodynamic Loads and Power under Single Wake Conditions

The contribution of wake meandering and shape asymmetry to load and power estimates is quantified by comparing aeroelastic simulations initialized with different inflow conditions: an axisymmetric base wake, an unsteady stochastic shape wake, and a large-eddy simulation with rotating actuator-line turbine representation. Time series of blade-root and tower base bending moments are analyzed. We find that meandering has a large contribution to the fluctuation of the loads. Moreover, considering the wake edge intermittence via the stochastic shape model improves the simulation of load and power fluctuations and of the fatigue damage equivalent loads. These results indicate that the stochastic shape wake simulator is a valuable addition to simplified wake models when seeking to obtain higher-fidelity computationally inexpensive predictions of loads and power.

Profiles of Wind and Turbulence in the Coastal Atmospheric Boundary Layer of Lake Erie

Prediction of wind resource in coastal zones is difficult due to the complexity of flow in the coastal atmospheric boundary layer (CABL). A three week campaign was conducted over Lake Erie in May 2013 to investigate wind characteristics and improve model parameterizations in the CABL. Vertical profiles of wind speed up to 200 m were measured onshore and offshore by lidar wind profilers, and horizontal gradients of wind speed by a 3-D scanning lidar. Turbulence data were collected from sonic anemometers deployed onshore and offshore. Numerical simulations were conducted with the Weather Research Forecasting (WRF) model with 2 nested domains down to a resolution of 1-km over the lake. Initial data analyses presented in this paper investigate complex flow patterns across the coast. Acceleration was observed up to 200 m above the surface for flow coming from the land to the water. However, by 7 km off the coast the wind field had not yet reached equilibrium with the new surface (water) conditions. The surface turbulence parameters over the water derived from the sonic data could not predict wind profiles observed by the ZephlR lidar located offshore. Horizontal wind speed gradients near the coast show the influence of atmospheric stability on flow dynamics. Wind profiles retrieved from the 3-D scanning lidar show evidence of nocturnal low level jets (LLJs). The WRF model was able to capture the occurrence of LLJ events, but its performance varied in predicting their intensity, duration, and the location of the jet core.

Effect of Wind Turbine Wakes on the Performance of a Real Case WRF-LES Simulation

The main objective of this work is to estimate how much of the discrepancy between measured and modeled flow parameters can be attributed to wake effects. The real case simulations were performed for a period of 15 days with the Weather Research and Forecasting (WRF) model and nested down to a Large-Eddy Simulation (LES) scale of ~ 100 m. Beyond the coastal escarpment, the site is flat and homogeneous and the study focuses on a meteorological mast and a northern turbine subjected to the wake of a southern turbine. The observational data set collected during the Prince Edward Island Wind Energy Experiment (PEIWEE) includes a sonic anemometer at 60 m mounted onto the mast, and measurements from the two turbines. Wake versus free stream conditions are distinguished based on measured wind direction while assuming constant expansion for the wake of the southern turbine. During the period considered the mast and northern turbine were under the southern turbine wake ~ 16% and ~ 11% of the time, respectively. Under these conditions, the model overestimates the wind speed and underestimates the turbulence intensity at the mast but not at the northern turbine, where the effect of wakes on the model error is unclear and other model limitations are likely more important. The wind direction difference between the southern and northern turbines is slightly underestimated by the model regardless of whether free stream or wake conditions are observed, indicating that it may be due to factors unrelated to the wake development such as surface forcings. Finally, coupling an inexpensive wake model to the high-fidelity simulation as a post-processing tool drives the simulated wind speeds at the mast significantly closer to the observed values, but the opposite is true at the coastal turbine which is in the far wake. This indicates that the application of a post-processing wake correction should be performed with caution and may increase the wind speed errors when other important sources of uncertainty in the model and data are not considered.

BEST PRACTICE FOR MEASURING WIND SPEEDS AND TURBULENCE OFFSHORE THROUGH IN-SITU AND REMOTE SENSING TECHNOLOGIES

Acknowledgment: “This material is based upon work supported by the Department of Energy under Award Number #DE-EE0005379.” Disclaimer: “This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.”