Volumetric LiDAR Scanning of a Wind Turbine Wake and Comparison with a 3D Analytical Wake Model

A correct estimation of the future power production is of capital importance whenever the feasibility of a future wind farm is being studied. This power estimation relies mostly on three aspects: (1) a reliable measurement of the wind resource in the area, (2) a well-established power curve of the future wind turbines and, (3) an accurate characterization of the wake effects; the latter being arguably the most challenging one due to the complexity of the phenomenon and the lack of extensive full-scale data sets that could be used to validate analytical or numerical models. The current project addresses the problem of obtaining a volumetric description of a full-scale wake of a 2MW wind turbine in terms of velocity deficit and turbulence intensity using three scanning wind LiDARs and two sonic anemometers. The characterization of the upstream flow conditions is done by one scanning LiDAR and two sonic anemometers, which have been used to calculate incoming vertical profiles of horizontal wind speed, wind direction and an approximation to turbulence intensity, as well as the thermal stability of the atmospheric boundary layer. The characterization of the wake is done by two scanning LiDARs working simultaneously and pointing downstream from the base of the wind turbine. The direct LiDAR measurements in terms of radial wind speed can be corrected using the upstream conditions in order to provide good estimations of the horizontal wind speed at any point downstream of the wind turbine. All this data combined allow for the volumetric reconstruction of the wake in terms of velocity deficit as well as turbulence intensity. Finally, the predictions of a 3D analytical model [1] are compared to the 3D LiDAR measurements of the wind turbine. The model is derived by applying the laws of conservation of mass and momentum and assuming a Gaussian distribution for the velocity deficit in the wake. This model has already been validated using high resolution wind-tunnel measurements and large-eddy simulation (LES) data of miniature wind turbine wakes, as well as LES data of real-scale wind-turbine wakes, but not yet with full-scale wind turbine wake measurements. [1] M. Bastankhah and F. Porté-Agel. A New Analytical Model For Wind-Turbine Wakes, in Renewable Energy, vol. 70, p. 116-123, 2014.

Volumetric LiDAR scanning of a wind turbine wake and comparison with a 3D analytical wake model

Fernando Carbajo Fuertes, Fernando Porté Agel

2016

Potential and uncertainty of wind energy in the Swiss Alps

Albertus Christiaan Kruyt

Switzerland has committed itself to an ambitious energy strategy. It aims to replace the exist- ing nuclear generation capacity with predominantly indigenous renewable resources. Wind power could play a significant role in this transition, yet the wind resource in the mountainous terrain that makes up most of the country is poorly understood. There are indications that this resource could be significant, but studies undertaken so far acknowledge large uncertain- ties. This is because the complex topography of the mountains influences the flow patterns significantly, and these can become partially decoupled from the synoptic flow aloft. This thesis aims to improve the understanding of the wind resource in highly complex terrain, and thereby contribute to a well informed energy transition in Switzerland. We start out by investigating the characteristics of the Swiss wind resource based on data from two meteorological measurement networks. From the pair-wise correlation between stations, it is concluded that wind farms across the country can be combined to produce a stable power output. It is also shown that elevation plays an important role in the wind resource, with the likelihood of sustained low wind speeds decreasing as a function of elevation, while mean speeds tend to increase with elevation. Next, a state of the art Numerical Weather Prediction model is assessed in its ability to simulate wind speeds over the Alps, and is shown to improve drastically upon existing mean wind speed estimates. This same model is then used to calculate the wind turbine capacity that is required to produce significant amounts of wind power, and it is found that the required capacity can be significantly reduced by allowing for wind turbines to be built at high elevations. In the last part of this thesis, smaller areas of the Alpine domain are simulated at high resolu- tions, to investigate the effect of increased model resolution on the accuracy and height of resource assessments. While it is found that optimal model parameterization is dependent on weather and terrain, strong indications of higher wind power potential are found with high resolution models compared to a model at lower resolution. This is explained by the fact that high resolutions are required to properly resolve the complex topography, which has a significant influence on the flow patterns and therefore, on the potential energy production.

EPFL2019

Incoming Wind and Wake Measurements of a Single 2.5 MW Wind Turbine Using Two Nacelle-Mounted Wind LiDARs for Analytical Wake Model Validation

Fernando Carbajo Fuertes, Corey Dean Markfort, Fernando Porté Agel

The two main effects of a wind turbine wake on those within its region of influence are: first, the wind speed deficit results in a decrease in the power production and second, a more turbulent flow results in higher unsteady structural forces, potentially reducing the lifespan of the turbines. Accurate quantification and modeling of wind turbine wakes is of utmost importance during the planning phase of new wind farms in order to optimize the layout and to accurately estimate the future power production. Whether wind turbine wake models are analytical, numerical, empirical, or a mixture, they need to be validated with experimental data. Wind tunnel experiments present some advantages for validation purposes (e.g. repeatability, flow control, wind turbine control, wind farm layout, etc.) although incomplete similarity limits the validity of the comparison. Ideally, model validation would be complemented with measurements of full-scale wind turbine wakes. The measurement technique that likely provides the most complete characterization of the wake is pulsed wind LiDAR, due to the relatively high spatial and temporal resolutions that can be achieved with a maximum measurement range up to a few kilometers. We will present preliminarily analysis from a measurement campaign that uses two nacelle-mounted, pulsed scanning LiDARs. The first LIDAR is oriented upwind to characterize vertical profiles of wind speed, wind direction, and yaw angle, as well as longitudinal and transversal turbulence intensity. The second LiDAR is oriented downwind to measure the horizontal field of the longitudinal wind in the wake region, including the average and standard deviation of the wind as shown in the left part of the figure below. The far wake of a wind turbine is defined by the self-similarity of the velocity deficit profile, which has been shown to follow a Gaussian shape. Fitting a Gaussian function to the velocity deficit allows for the study of the near wake length and the evolution of the maximum velocity deficit as well as the wake width at different distances downstream of the wind turbine. The results demonstrate the advantage of the dual LiDAR setup and possible data treatment. The full-scale measurements can help us to understand how higher atmospheric turbulence conditions result in a shortened length of the near wake and increased growth rate of the far wake. Data from the full-scale experiment are presented in the right part of the figure, together with results from validated LES simulations and a wind tunnel experiment, and can be used to validate analytical wake models [1,2] derived from the laws of conservation of mass and momentum and assuming a Gaussian distribution for the wake velocity deficit. [1] M. Bastankhah and F. Porté-Agel. A New Analytical Model For Wind-Turbine Wakes, in Renewable Energy, vol. 70, p. 116-123, 2014. [2] M. Bastankhah and F. Porté-Agel. Experimental and theoretical study of wind turbine wakes in yawed conditions. Journal of Fluid Mechanics, 806, 506–541, 2016. Figure caption: the left part of the figure presents an example of the reconstruction of the wake of a 2.5 MW wind turbine for a period of 30 minutes in terms of the longitudinal velocity deficit (top) and the local longitudinal turbulence intensity (bottom) for low turbulence inflow conditions. The right part of the figure presents the relationship of the spanwise wake growth rate (top) and the length of the near wake (bottom) with the longitudinal turbulence intensity. The setup and technique used do not allow to calculate near wake lengths shorter than 2D. This is represented by the shaded region on the bottom right part of the figure.

2018