The spectral signature of wind turbine wake meandering: A wind tunnel and field‐scale study

Field‐scale and wind tunnel experiments were conducted in the 2D to 6D turbine wake region to investigate the effect of geometric and Reynolds number scaling on wake meandering. Five field deployments took place: 4 in the wake of a single 2.5‐MW wind turbine and 1 at a wind farm with numerous 2‐MW turbines. The experiments occurred under near‐neutral thermal conditions. Ground‐based lidar was used to measure wake velocities, and a vertical array of met‐mounted sonic anemometers were used to characterize inflow conditions. Laboratory tests were conducted in an atmospheric boundary layer wind tunnel for comparison with the field results. Treatment of the low‐resolution lidar measurements is discussed, including an empirical correction to velocity spectra using colocated lidar and sonic anemometer. Spectral analysis on the laboratory‐ and utility‐scale measurements confirms a meandering frequency that scales with the Strouhal number St = fD/U based on the turbine rotor diameter D. The scaling indicates the importance of the rotor‐scaled annular shear layer to the dynamics of meandering at the field scale, which is consistent with findings of previous wind tunnel and computational studies. The field and tunnel spectra also reveal a deficit in large‐scale turbulent energy, signaling a sheltering effect of the turbine, which blocks or deflects the largest flow scales of the incoming flow. Two different mechanisms for wake meandering—large scales of the incoming flow and shear instabilities at relatively smaller scales—are discussed and inferred to be related to the turbulent kinetic energy excess and deficit observed in the wake velocity spectra.     

Performance and wake comparison of horizontal and vertical axis wind turbines under varying surface roughness conditions

A numerical study of both a horizontal axis wind turbine (HAWT) and a vertical axis wind turbine (VAWT) with similar size and power rating is presented. These large scale turbines have been tested when operating stand‐alone at their optimal tip speed ratio (TSR) within a neutrally stratified atmospheric boundary layer (ABL). The impact of three different surface roughness lengths on the turbine performance is studied for the both turbines. The turbines performance, the response to the variation in the surface roughness of terrain, and the most relevant phenomena involved on the resulting wake were investigated. The main goal was to evaluate the differences and similarities of these two different types of turbine when they operate under the same atmospheric flow conditions. An actuator line model (ALM) was used together with the large eddy simulation (LES) approach for predicting wake effects, and it was implemented using the open‐source computational fluid dynamics (CFD) library OpenFOAM to solve the governing equations and to compute the resulting flow fields. This model was first validated using wind tunnel measurements of power coefficients and wake of interacting HAWTs, and then employed to study the wake structure of both full scale turbines. A preliminary study test comparing the forces on a VAWT blades against measurements was also investigated. These obtained results showed a better performance and shorter wake (faster recovery) for an HAWT compared with a VAWT for the same atmospheric conditions.     

On atmospheric stability in the dynamic wake meandering model

The present study investigates a new approach for capturing the effects of atmospheric stability on wind turbine wake evolution and wake meandering by using the dynamic wake meandering model. The most notable impact of atmospheric stability on the wind is the changes in length and velocity scales of the atmospheric turbulence. The length and velocity scales in the turbulence are largely responsible for the way in which wind turbine wakes meander as they convect downstream. The hypothesis of the present work is that appropriate turbulence scales can be extracted from the oncoming atmospheric turbulence spectra and applied to the dynamic wake meandering model to capture the correct wake meandering behaviour. The ambient turbulence in all stability classes is generated using the Mann turbulence model, where the effects of non‐neutral atmospheric stability are approximated by the selection of input parameters. In order to isolate the effect of atmospheric stability, simulations of neutral and unstable atmospheric boundary layers using large‐eddy simulation are performed at the same streamwise turbulence intensity level. The turbulence intensity is kept constant by calibrating the surface roughness in the computational domain. The changes in the turbulent length scales due to the various atmospheric stability states impact the wake meandering characteristics and thus the power generation by the individual turbines. The proposed method is compared with results from both large‐eddy simulation coupled with an actuator line model and field measurements, where generally good agreement is found with respect to the velocity, turbulence intensity and power predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind‐turbine wakes for different hub‐height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met‐tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub‐height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near‐wake velocity field, while it affects noticeably the far‐wake evolution and recovery. A secondary effect on wake‐recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake‐generated turbulence fosters wake recovery. A semi‐empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models.     

Reynolds number dependence of turbulence statistics in the wake of wind turbines

A wind tunnel experiment has been performed to quantify the Reynolds number dependence of turbulence statistics in the wake of a model wind turbine. A wind turbine was placed in a boundary layer flow developed over a smooth surface under thermally neutral conditions. Experiments considered Reynolds numbers on the basis of the turbine rotor diameter and the velocity at hub height, ranging from Re = 1.66 × 10⁴ to 1.73 × 10⁵. Results suggest that main flow statistics (mean velocity, turbulence intensity, kinematic shear stress and velocity skewness) become independent of Reynolds number starting from Re ≈ 9.3 × 10⁴. In general, stronger Reynolds number dependence was observed in the near wake region where the flow is strongly affected by the aerodynamics of the wind turbine blades. In contrast, in the far wake region, where the boundary layer flow starts to modulate the dynamics of the wake, main statistics showed weak Reynolds dependence. These results will allow us to extrapolate wind tunnel and computational fluid dynamic simulations, which often are conducted at lower Reynolds numbers, to full‐scale conditions. In particular, these findings motivates us to improve existing parameterizations for wind turbine wakes (e.g. velocity deficit, wake expansion, turbulence intensity) under neutral conditions and the predictive capabilities of atmospheric large eddy simulation models. Copyright © 2011 John Wiley & Sons, Ltd.     

On the application of the Jensen wake model using a turbulence‐dependent wake decay coefficient: the Sexbierum case

We present a methodology to process wind turbine wake simulations, which are closely related to the nature of wake observations and the processing of these to generate the so‐called wake cases. The method involves averaging a large number of wake simulations over a range of wind directions and partly accounts for the uncertainty in the wind direction assuming that the same follows a Gaussian distribution. Simulations of the single and double wake measurements at the Sexbierum onshore wind farm are performed using a fast engineering wind farm wake model based on the Jensen wake model, a linearized computational fluid dynamics wake model by Fuga and a nonlinear computational fluid dynamics wake model that solves the Reynolds‐averaged Navier–Stokes equations with a modified k‐ε turbulence model. The best agreement between models and measurements is found using the Jensen‐based wake model with the suggested post‐processing. We show that the wake decay coefficient of the Jensen wake model must be decreased from the commonly used onshore value of 0.075 to 0.038, when applied to the Sexbierum cases, as wake decay is related to the height, roughness and atmospheric stability and, thus, to turbulence intensity. Based on surface layer relations and assumptions between turbulence intensity and atmospheric stability, we find that at Sexbierum, the atmosphere was probably close to stable, although the stability was not observed. We support these assumptions using detailed meteorological observations from the Høvsøre site in Denmark, which is topographically similar to the Sexbierum region. © 2015 The Authors. Wind Energy published by John Wiley & Sons Ltd.     

Atmospheric stability‐dependent infinite wind‐farm models and the wake‐decay coefficient

We extend the infinite wind‐farm boundary‐layer (IWFBL) model of Frandsen to take into account atmospheric static stability effects. This extended model is compared with the IWFBL model of Emeis and to the Park wake model used in Wind Atlas Analysis and Application Program (WAsP), which is computed for an infinite wind farm. The models show similar behavior for the wind‐speed reduction when accounting for a number of surface roughness lengths, turbine to turbine separations and wind speeds under neutral conditions. For a wide range of atmospheric stability and surface roughness length values, the extended IWFBL model of Frandsen shows a much higher wind‐speed reduction dependency on atmospheric stability than on roughness length (roughness has been generally thought to have a major effect on the wind‐speed reduction). We further adjust the wake‐decay coefficient of the Park wake model for an infinite wind farm to match the wind‐speed reduction estimated by the extended IWFBL model of Frandsen for different roughness lengths, turbine to turbine separations and atmospheric stability conditions. It is found that the WAsP‐recommended values for the wake‐decay coefficient of the Park wake model are (i) larger than the adjusted values for a wide range of neutral to stable atmospheric stability conditions, a number of roughness lengths and turbine separations lower than ～ 10 rotor diameters and (ii) too large compared with those obtained by a semiempirical formulation (relating the ratio of the friction to the hub‐height free velocity) for all types of roughness and atmospheric stability conditions. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm

Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantification of power losses due to wind turbine wake interactions through SCADA,meteorological and wind LiDAR data

Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.     

Large‐eddy simulation of stable boundary layer turbulence and estimation of associated wind turbine loads

Stochastic simulation of turbulent inflow fields commonly used in wind turbine load computations is unable to account for contrasting states of atmospheric stability. Flow fields in the stable boundary layer, for instance, have characteristics such as enhanced wind speed and directional shear; these effects can influence loads on utility‐scale wind turbines. To investigate these influences, we use large‐eddy simulation (LES) to generate an extensive database of high‐resolution ( ～ 10 m), four‐dimensional turbulent flow fields. Key atmospheric conditions (e.g., geostrophic wind) and surface conditions (e.g., aerodynamic roughness length) are systematically varied to generate a diverse range of physically realizable atmospheric stabilities. We show that turbine‐scale variables (e.g., hub height wind speed, standard deviation of the longitudinal wind speed, wind speed shear, wind directional shear and Richardson number) are strongly interrelated. Thus, we strongly advocate that these variables should not be prescribed as independent degrees of freedom in any synthetic turbulent inflow generator but rather that any turbulence generation procedure should be able to bring about realistic sets of such physically realizable sets of turbine‐scale flow variables. We demonstrate the utility of our LES‐generated database in estimation of loads on a 5‐MW wind turbine model. More importantly, we identify specific turbine‐scale flow variables that are responsible for large turbine loads—e.g., wind speed shear is found to have a greater influence on out‐of‐plane blade bending moments for the turbine studied compared with its influence on other loads such as the tower‐top yaw moment and the fore‐aft tower base moment. Overall, our study suggests that LES may be effectively used to model inflow fields, to study characteristics of flow fields under various atmospheric stability conditions and to assess turbine loads for conditions that are not typically examined in design standards. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of the effects of turbulence model enhancements on wind turbine wake predictions

The modelling of wind turbine wakes is investigated in this paper using a Navier–Stokes solver employing the k–ω turbulence model appropriately modified for atmospheric flows. It is common knowledge that even single‐wind turbine wake predictions with computational fluid dynamic methods underestimate the near wake deficit, directly contributing to the overestimation of the power of the downstream turbines. For a single‐wind turbine, alternative modelling enhancements under neutral and stable atmospheric conditions are tested in this paper to account for and eventually correct the turbulence overestimation that is responsible for the faster flow recovery that appears in the numerical predictions. Their effect on the power predictions is evaluated with comparison with existing wake measurements. A second issue addressed in this paper concerns multi‐wake predictions in wind farms, where the estimation of the reference wind speed that is required for the thrust calculation of a turbine located in the wake(s) of other turbines is not obvious. This is overcome by utilizing an induction factor‐based concept: According to it, the definition of the induction factor and its relationship with the thrust coefficient are employed to provide an average wind speed value across the rotor disk for the estimation of the axial force. Application is made on the case of five wind turbines in a row. Copyright © 2010 John Wiley & Sons, Ltd.     

An experimental investigation on the effect of individual turbine control on wind farm dynamics

Individual wind turbines in a wind farm typically operate to maximize their performance with no consideration of the impact of wake effects on downstream turbines. There is potential to increase power and reduce structural loads within a wind farm by properly coordinating the turbines. To effectively design and analyze coordinated wind turbine controllers requires control‐oriented turbine wake models of sufficient accuracy. This paper focuses on constructing such a model from experiments. The experiments were conducted to better understand the wake interaction and impact on voltage production in a three‐turbine array. The upstream turbine operating condition was modulated in time, and the dynamic impact on the downstream turbine was recorded through the voltage output time signal. The flow dynamics observed in the experiments were used to improve a static wake model often used in the literature for wind farm control. These experiments were performed in the atmospheric boundary layer wind tunnel at the Saint Anthony Falls Laboratory at the University of Minnesota using particle image velocimetry for flow field analysis and turbine voltage modulation to capture the physical evolution in addition to the dynamics of turbine wake interactions. Copyright © 2015 John Wiley & Sons, Ltd.     

Upwind preview to a horizontal axis wind turbine: a wind tunnel and field‐scale study

Data collected at the Eolos wind research facility and in the Saint Anthony Falls Laboratory atmospheric boundary layer wind tunnel are used to study the impact of turbulent inflow conditions on the performance of a horizontal axis wind turbine on flat terrain. The Eolos test facility comprises a 2.5MW Clipper Liberty C96 wind turbine, a meteorological tower and a WindCube LiDAR wind profiler. A second set of experiments was completed using particle image velocimetry upwind and in a wake of a miniature turbine in the wind tunnel to complement LiDAR measurements near the Eolos turbine. Joint statistics, most notably temporal cross‐correlations between wind velocity at different heights and turbine performance, are presented and compared at both the laboratory and field scales. The work (i) confirms that the turbine exerts a blockage effect on the mean flow and (ii) suggests a key, specific elevation, above hub height, where the incoming velocity signal is statistically most relevant to turbine operation and control. Wind tunnel measurements confirm such indication and suggest that hub height velocity measurements are optimal for wind preview and/or as input for active control strategies in aligned turbine configurations. Copyright © 2015 John Wiley & Sons, Ltd.     

Investigation of wake interaction using full‐scale lidar measurements and large eddy simulation

In this paper, wake interaction resulting from two stall regulated turbines aligned with the incoming wind is studied experimentally and numerically. The experimental work is based on a full‐scale remote sensing campaign involving three nacelle mounted scanning lidars. A thorough analysis and interpretation of the measurements is performed to overcome either the lack of or the poor calibration of relevant turbine operational sensors, as well as other uncertainties inherent in resolving wakes from full‐scale experiments. The numerical work is based on the in‐house EllipSys3D computational fluid dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation is based on a comparison between wake deficit, wake generated turbulence, turbine power production and thrust force. An excellent agreement between measurement and simulation is seen in both the fixed and the meandering frame of reference. Copyright © 2015 John Wiley & Sons, Ltd.     

Characterizing the response of a wind turbine model under complex inflow conditions

A horizontal axis wind turbine model was tested in a closed‐circuit wind tunnel under various inflow conditions. Separate experiments placed the test turbine (i) in the wake of a three‐dimensional, sinusoidal hill, (ii) in the wake of another turbine and (iii) in the turbulent boundary layer, as a reference case. Simultaneous high‐frequency measurements of the turbine output voltage, rotor angular velocity along with streamwise and wall normal velocity components were collected at various locations through the turbine's miniature direct‐current (DC) generator, a high‐resolution laser tachometer and cross‐wire anemometer, respectively. Validation trials were conducted first in order to characterize the test turbine's output and response to the baseline turbulent boundary layer. Analysis was performed by comparing the cross‐wire anemometry measurements of the incoming flow with the turbine voltage output to investigate the unsteady rotor kinematics under different flow perturbations. Using spectral, auto‐correlation and cross‐correlation methods, it was found that the flow structures developing downwind of the hill leave a stronger signature on the fluctuations and spectrum of the rotor angular velocity, as compared with those flow structures filtered or deflected by placing a turbine upwind. In summary, we show that the effects on downwind turbines of complex terrain and multi‐turbine arrangements are consistent with the induced modifications by the hill or turbine on the large scale structures in the incoming flow. Copyright © 2014 John Wiley & Sons, Ltd.     

Numerical investigation of wind turbine wake development in directionally sheared inflow

Turbines in wind farms are subject to complex mutual aerodynamic interactions, which in detail depend upon the characteristics of the atmospheric boundary layer. Our two objectives with this paper were to investigate the impact of directionally sheared inflow on the wake development behind a single wind turbine and to analyse the impact of the wakes on the energy yield and loading of a downstream turbine, which is exposed to partial and full wake conditions. We performed simulations with a framework based on a coupled approach of large‐eddy simulation and an actuator line representation of an aeroelastic turbine model. Our results show that directionally sheared inflow leads to a non‐symmetrical wake development, which transfers to distinct differences in the energy yield and loading of downstream turbines of equal lateral offsets in opposite direction. Therefore, the assumption of wakes being axisymmetrical could lead to notable deviations in the prediction of wake behaviour and their impact on downstream turbines for atmospheric inflow conditions, which include directional shear. Copyright © 2016 John Wiley & Sons, Ltd.     

Coupling Doppler radar‐derived wind maps with operational turbine data to document wind farm complex flows

The first known dual‐Doppler (DD) measurements collected within a utility‐scale wind farm are presented. Various complex flow features are discussed, including detailed analyses of turbine wakes, turbine‐to‐turbine interaction, high wind speed channels that exist between individual wakes and intermittent gust propagation. The data have been collected using innovative mobile Doppler radar technologies, which allows for a large observational footprint of ~17 km² in the presented analyses while maintaining spatial resolution of 0.49° in the azimuthal dimension by 15 m in the along‐beam range dimension. The presented DD syntheses provide three‐dimensional fields of the horizontal wind speed and direction with a revisit time of approximately 1 min. DD wind fields are validated with operational turbine data and are successfully used to accurately project composite power output for several turbines. The employed radar technologies, deployment schemes, scanning strategies and subsequent analysis methodologies offer the potential to contribute to the validation and improvement of current wake modeling efforts that influence wind farm design and layout practices, enhanced resource assessment campaigns, and provide real‐time wind maps to drive ‘smart’ wind farm operation. Copyright © 2014 John Wiley & Sons, Ltd.     

Characterizing power performance and wake of a wind turbine under yaw and blade pitch

Wind turbine wakes have been recognized as a key issue causing underperformance in existing wind farms. In order to improve the performance and reduce the cost of energy from wind farms, one approach is to develop innovative methods to improve the net capacity factor by reducing wake losses. The output power and characteristics of the wake of a utility‐scale wind turbine under yawed flow is studied to explore the possibility of improving the overall performance of wind farms. Preliminary observations show that the power performance of a turbine does not degrade significantly under yaw conditions up to approximately 10°. Additionally, a yawed wind turbine may be able to deflect its wake in the near‐wake region, changing the wake trajectory downwind, with the progression of the far wake being dependent on several atmospheric factors such as wind streaks. Changes in the blade pitch angle also affect the characteristics of the turbine wake and are also examined in this paper. Copyright © 2015 John Wiley & Sons, Ltd.     

Self‐similarity and turbulence characteristics of wind turbine wakes via large‐eddy simulation

Mean and turbulent properties of the wake generated by a single wind turbine are studied in this paper with a new large eddy simulation (LES) code, the wind turbine and turbulence simulator (WiTTS hereafter). WiTTS uses a scale‐dependent Lagrangian dynamical model of the sub‐grid shear stress and actuator lines to simulate the effects of the rotating blades. WiTTS is first tested by simulating neutral boundary layers without and with a wind turbine and then used to study the common assumptions of self‐similarity and axisymmetry of the wake under neutral conditions for a variety of wind speeds and turbine properties. We find that the wind velocity deficit generally remains self similarity to a Gaussian distribution in the horizontal. In the vertical, the Gaussian self‐similarity is still valid in the upper part of the wake, but it breaks down in the region of the wake close to the ground. The horizontal expansion of the wake is always faster and greater than the vertical expansion under neutral stability due to wind shear and impact with the ground. Two modifications to existing equations for the mean velocity deficit and the maximum added turbulence intensity are proposed and successfully tested. The anisotropic wake expansion is taken into account in the modified model of the mean velocity deficit. Turbulent kinetic energy (TKE) budgets show that production and advection exceed dissipation and turbulent transport. The nacelle causes significant increase of every term in the TKE budget in the near wake. In conclusion, WiTTS performs satisfactorily in the rotor region of wind turbine wakes under neutral stability. Copyright © 2014 John Wiley & Sons, Ltd.     

Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm

This paper investigates wake effects on load and power production by using the dynamic wake meander (DWM) model implemented in the aeroelastic code HAWC2. The instationary wind farm flow characteristics are modeled by treating the wind turbine wakes as passive tracers transported downstream using a meandering process driven by the low frequent cross‐wind turbulence components. The model complex is validated by comparing simulated and measured loads for the Dutch Egmond aan Zee wind farm consisting of 36 Vestas V90 turbine located outside the coast of the Netherlands. Loads and production are compared for two distinct wind directions—a free wind situation from the dominating southwest and a full wake situation from northwest, where the observed turbine is operating in wake from five turbines in a row with 7D spacing. The measurements have a very high quality, allowing for detailed comparison of both fatigue and min–mean–max loads for blade root flap, tower yaw and tower bottom bending moments, respectively. Since the observed turbine is located deep inside a row of turbines, a new method on how to handle multiple wakes interaction is proposed. The agreement between measurements and simulations is excellent regarding power production in both free and wake sector, and a very good agreement is seen for the load comparisons too. This enables the conclusion that wake meandering, caused by large scale ambient turbulence, is indeed an important contribution to wake loading in wind farms. Copyright © 2012 John Wiley & Sons, Ltd.