H Wang

Wind Measurements from Arc Scans with Doppler Wind Lidar

AbstractDefining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity ar...

Wind turbine wake detection with a single Doppler wind lidar

Using scanning lidar wind turbine wakes can be probed in three dimensions to produce a wealth of temporally and spatially irregular data that can be used to characterize the wakes. Unlike data from a meteorological mast or upward pointing lidar, the spatial coordinates of the measurements are not fixed and the location of the wake also varies in three dimensions. Therefore the challenge is to provide automated detection algorithms to identify wakes and quantify wake characteristics from this type of dataset. Here an algorithm is developed and evaluated on data from a large wind farm in the Midwest. A scanning coherent Doppler wind lidar was configured to measure wind speed in the wake of a continuously yawing wind turbine for two days during the experiment and wake profiles were retrieved with input of wind direction information from the nearby meteorological mast. Additional challenges to the analysis include incomplete coverage of the entire wake due to the limited scanning domain, and large wind shear that can contaminate the wake estimate because of the height variation along the line-of-sight. However, the algorithm developed in this paper is able to automatically capture wakes in lidar data from Plan Position Indicator (PPI) scans and the resultant wake statistics are consistent with previous experiments results.

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.

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.”