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

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.

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

Fernando Carbajo Fuertes, Fernando Porté Agel

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.

2016

Simulation of atmospheric flows over complex terrain for wind power potential assessment

When assessing the economic viability of a wind farm, the estimation of the on-site wind power potential is perhaps the most important step. The most common way of evaluating the wind power potential of an area of interest consists of making on-site measurements for a period of one year. In order to take account of the inter-annual variation of wind speed, the one year of data are normally correlated with data recorded at a reference site where long-term data (typically > 10 years) are available. A correlation analysis is formulated for the concurrent data sets at the reference and prediction sites. This correlation is then used to transform the long-term wind speed at the reference site to the long-term wind speed that would have been expected at the prediction site had long-term measurements been made at this site. An alternative approach is also used, which consists of establishing site-to-site relationships using a numerical model to simulate meteorological situations which are typical for the area of interest. These relationships are then used to transpose the known long-term wind statistics of the reference site to the prediction site. Such an approach is applied in this work to the region of Chasseral & Mt-Crosin. The wind data available for a period of 16 years at Chasseral are transposed to the Mt-Crosin site where they are then compared to the data measured at the location of the installed wind farm. Over complex terrain, the linearised models traditionally used for wind power potential assessment fail to reproduce accurate wind fields. Therefore, to be applied to mountainous terrain such as that found in Switzerland, the approach relying on numerical simulation requires the development and validation of a numerical tool capable of simulating wind fields over complex topography. As the numerical model would have to deal with relatively steep slopes requiring a fine horizontal (50-100 m) and vertical resolution (∼5-10 m in the lowest levels), a fluid dynamics model was used which solves the complete set of Navier-Stokes equations with κ-ε turbulence closure. The standard version of the model used (CFX4) is modified in a novel way to extend its field of application so that atmospheric phenomena could be simulated which are typical of the meso-scale. The modified version solves the flow equations with the anelastic approximation (deep Boussinesq) and assuming a background rotation of the wind field (with the high altitude wind field following the geostrophic approximation). In the first part of this work, the numerical model is validated. The results obtained in this phase show that for meteorological situations for which the wind at the ground is coupled to the high altitude wind, the numerical model is able to satisfactorily reproduce: the flow in the surface layer, reproducing the effects associated with the ground roughness, roughness change, or heat flux through the ground; the flow in the Ekman layer together with the interaction between the free flow thermal stability conditions and the boundary layer; the linear and non-linear effects associated with the perturbation induced by a mountain in a stably stratified flow. In the second stage of this work, an extension of the standard Measure-Correlate-Predict method is presented to calculate the wind speed distribution at the prediction site, from transposition relationships and from the wind statistics at the reference site. The validity of the underlying assumptions is confirmed using concurrent data sets that were collected at both the reference and prediction sites. To evaluate the accuracy that can be achieved with the transposition assumptions, a back-prediction is performed using the transposition relationships obtained using the observations. Different types of transposition relationships have been investigated. Finally, the transposition methodology is applied to calculate the wind speed conditions at Mt-Crosin from the Chassera1 data, using the transposition relationships calculated by the numerical model for a range of meteorological situations typical for the area considered. The Mt-Crosin to Chassera1 sector wind speed ratios calculated by the numerical model tend to slightly underestimate those observed. The mean wind speeds obtained from the transposition are underestimated by 7% to 18% at the three measuring mast locations on Mt-Crosin. The yearly energy output that can be produced by a wind turbine in these conditions is underestimated by 8% to 36%. For a further period, the actual energy production of the three installed wind turbines has been compared with the model prediction at hub height, which showed that the transposition results underestimate the actual yearly production by 22% to 24%. From the transposition of the long-term data at Chassera1 (16 years), with the relationships obtained by the numerical model, a wind power potential of between 470 MWh/year (Côte Est) and 596 MWh/year (Côte Nord) is predicted using the characteristics of a Vestas-V44 wind turbine. From the work presented here, it appears that for well-exposed sites such as those located along the Jura Crest, the methods developed are able to give a wind power potential prediction with a similar accuracy as a one year measurement campaign performed on site.

EPFL1998

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

Fernando Carbajo Fuertes, Fernando Porté Agel

2016

Simulation of atmospheric flows over complex terrain for wind power potential assessment

EPFL1998