No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Volumetric Lidar Scanning of Wind Turbine Wakes under Convective and Neutral Atmospheric Stability Regimes
Abstract
Optimization of a wind farm's layout is a strategic task to reduce wake effects on downstream turbines, thus maximizing wind power harvesting. However, downstream evolution and recovery of each wind turbine wake are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as the vertical profiles of the mean wind velocity and the turbulence intensity, which are in turn affected by the ABL thermal stability. Therefore, the characterization of the variability of wind turbine wakes under different ABL stability regimes becomes fundamental to better predict wind power harvesting and to improve wind farm efficiency. To this aim, wind velocity measurements of the wake produced by a 2-MW Enercon E-70 wind turbine were performed with three scanning Doppler wind lidars. One lidar was devoted to the characterization of the incoming wind in particular, wind velocity, shear, and turbulence intensity at the height of the rotor disc. The other two lidars performed volumetric scans of the wind turbine wake under different atmospheric conditions. Through the evaluation of the minimum wake velocity deficit as a function of the downstream distance, it is shown that the ABL stability regime has a significant effect on the wake evolution; in particular, the wake recovers faster under convective conditions. This result suggests that atmospheric inflow conditions, and particularly thermal stability, should be considered for improved wake models and predictions of wind power harvesting.
... Since no instrument is available to provide actual freestream measurements, reference conditions are defined as the average of environmental conditions across all turbines not affected by wakes generated upstream (unwaked turbines), as proposed in a previous work studying the site under consideration [31]. This procedure is used to define reference conditions for hub-height wind speed, wind direction, and T I. Reference T I is found to have a strong daily cycle associated with the variation of atmospheric stability [35,36,37,38,39,40]. The probability density function of the reference hub-height wind speed is reported in figure 2(b). ...
... The magnitude of the slowdowns due to wakes is also consistent with other field studies performed for this wind farm [31]. As T I increases, the wakes become less prominent, and the peaks recede [32,40]. Eventually, the wake effects from the first and second rows become very diffuse and extend over much of the region between 90 • and 180 • . ...
... Wind speeds above the turbine rated wind speed have almost no wake losses and lie in region 3 of the power curve. Additionally, wake losses decrease in magnitude with increasing reference T I, as thoroughly documented in the literature [32,40,51]. The model can capture small details in the variability of power performance, such as power increases due to a combination of local wind speed and T I variation. ...
Different machine learning (ML) models are trained on SCADA and meteorological data collected at an onshore wind farm and then assessed in terms of fidelity and accuracy for predictions of wind speed, turbulence intensity, and power capture at the turbine and wind farm levels for different wind and atmospheric conditions. ML methods for data quality control and pre-processing are applied to the data set under investigation and found to outperform standard statistical methods. A hybrid model, comprised of a linear interpolation model, Gaussian process, deep neural network (DNN), and support vector machine, paired with a DNN filter, is found to achieve high accuracy for modeling wind turbine power capture. Modifications of the incoming freestream wind speed and turbulence intensity, TI, due to the evolution of the wind field over the wind farm and effects associated with operating turbines are also captured using DNN models. Thus, turbine-level modeling is achieved using models for predicting power capture while farm-level modeling is achieved by combining models predicting wind speed and TI at each turbine location from freestream conditions with models predicting power capture. Combining these models provides results consistent with expected power capture performance and holds promise for future endeavors in wind farm modeling and diagnostics. Though training ML models is computationally expensive, using the trained models to simulate the entire wind farm takes only a few seconds on a typical modern laptop computer, and the total computational cost is still lower than other available mid-fidelity simulation approaches.
... They can be nacelle-mounted (Bingöl et al., 2010;Trujillo et al., 2011) or ground-based, using combinations of profiling and scans to build up a 3D picture of the 50 wake (Banta et al., 2013;Barthelmie et al., 2014Barthelmie et al., , 2018. Dual Doppler lidars have been used to give greater accuracy of wind retrieval in the highly turbulent wake (Iungo et al., 2013;Iungo and Porté-Agel, 2014) or non-homogeneous flow over complex terrain (Vasiljević et al., 2017). Single scanning lidars have also been effective in analysing wake dimensions (Aitken et al., 2014;Bodini et al., 2017). ...
... For building B, this is perhaps because the previously demonstrated dependence on unstable conditions is averaged out by stable conditions within the lower wind speed classes. These results are again similar to the study by Iungo and Porté-Agel (2014), who did not find large differences in the length of the wind turbine wake for different background wind speeds. This provides confidence that the wake length of building B for φ ≈ 220 • is shorter due to unstable conditions. ...
High-rise buildings, increasingly a feature of many large cities, impact local atmospheric flow conditions. Tall building wakes affect air quality downstream due to turbulent mixing and require parametrization in dispersion models. Previous studies using numerical or physical modelling have been idealised and under neutral conditions. There has been a lack of data available in real urban environments due to the difficulty in deploying traditional wind sensors. Doppler wind lidars (DWLs) have been used frequently for studying wind turbine wakes but never building wakes. This study is a year-long deployment of a DWL in a complex urban environment studying tall building wakes under atmospheric conditions. A HALO Photonic Streamline DWL was deployed in a low- and mid-rise densely packed area in central London. From its roof-top position (33.5 m agl compared to mean building height 12.5 m), Velocity Azimuth Display (VAD) scans at zero-degree elevation intersected with two, taller nearby buildings of 90 and 40 m agl. Using an ensemble averaging approach, wake dimensions were investigated in terms of wind direction, stability and wind speed. Boundary-layer stability categories were defined using eddy covariance observations from the BT Tower (191 m) and mixing height estimations from vertical stare scans. A method for calculating normalised velocity deficit from VAD scans is presented. For neutral conditions, wake dimensions around both buildings for the prevailing wind direction were compared with the ADMS-Build wake model for a single, isolated cube. The model underpredicts wakes dimensions, confirming previous wind tunnel findings for the same area. Under varying stability, unstable and deep boundary layers were shown to produce shorter, narrower wakes. Typical observed wake lengths were 120–300 m and widths were 80–150 m and were reduced by 50–100 m downwind. Stable and shallow boundary layers were less frequent and produced an insignificant difference in wake dimensions to neutral conditions. The sensitivity to stability was weakened by enhanced turbulence upstream (i.e., due to other building wakes). Weakened stability dependence was confirmed if there were more obstacles upstream as the wind direction incident on the buildings changed. The results highlight the potential for future wake studies using multiple DWLs deploying both vertical and horizontal scan patterns. Dispersion models should incorporate the effect of a complex urban canopy within which tall buildings are embedded.
... Scanning lidars, i.e., those that feature the ability to direct the laser beam in arbitrary directions (among other abilties), offer additional flexibility and permit a more targeted characterization of the wind plant flow. These lidars have been extensively used, both on the ground and mounted on turbine nacelles, to characterize wakes in the horizontal (e.g., Trujillo et al., 2011;Kumer et al., 2015;Bodini et al., 2017;Zhan et al., 2019) and vertical plane (Iungo et al., 2013), as well as in a volumetric fashion (e.g., Iungo and Porté-Agel, 2014;Doubrawa et al., 2019;Letizia et al., 2021b). ...
... Figure 3A highlights a prevalence of studies focusing on the inflow, especially before 2015, boosted by a proliferation of publications regarding wind turbine control ( Figure 3B), a topic that received significant attention from the industrial community at that time, likely as a consequence of increased research funding and the creation of new lidar companies. The success of seminal field lidar campaigns (e.g., Bingöl, 2009;Krishnamurthy et al., 2013;Mikkelsen et al., 2013;Iungo and Porté-Agel, 2014;Machefaux et al., 2015) appears to be a key factor that raised the interest of the scientific community in this ...
This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.
... This was achieved since their design enables the acquisition of wind observations with high spatial and temporal resolution while operating either ground based (e.g. Iungo et al., 2013;Iungo and Porté-Agel, 2014;Archer et al., 2019;Menke et al., 2020) or nacelle mounted (e.g. Bingöl et al., 2008;Carbajo Fuertes et al., 2018;Schneemann et al., 2021;Cañadillas et al., 2022;Brugger et al., 2022). ...
... Bingöl et al., 2008;Carbajo Fuertes et al., 2018;Schneemann et al., 2021;Cañadillas et al., 2022;Brugger et al., 2022). Wind lidars are now used to detect the propagation of the wake trace (Archer et al., 2019) and, subsequently, measure both the mean (Iungo et al., 2013;Iungo and Porté-Agel, 2014;Menke et al., 2020) and the dynamic (Carbajo Fuertes et al., 2018) wake characteristics. Thus, they are used to evaluate the accuracy of different wake models (Trujillo et al., 2011;Trabucchi et al., 2017;Brugger et al., 2022). ...
We investigate the characteristics of the inflow and the wake of a 6 MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the pitch and roll angles of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterized in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor, and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals between 8.5–9.5 and 12.5–13.5 m s-1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %–6.4 %. The wake flow is measured along a horizontal plane by a wind lidar scanning in a plan position indicator mode, which reaches 10 D downwind. This study focuses on the downstream area between 3 and 8 D. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit that follows a Gaussian distribution. We find that even small variations (∼1 %–2 %) in the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine examined in this study.
... This was achieved since their design enables the acquisition of wind observations with high spatial and temporal resolution while operating either ground-based (e.g. Iungo et al., 2013;Iungo and Porté-Agel, 2014;Archer et al., 2019;Menke et al., 2020) or nacelle-mounted (e.g. Bingöl et al., 2008;Carbajo Fuertes et al., 2018;50 Schneemann et al., 2021;Cañadillas et al., 2022;Brugger et al., 2022). ...
... Bingöl et al., 2008;Carbajo Fuertes et al., 2018;50 Schneemann et al., 2021;Cañadillas et al., 2022;Brugger et al., 2022). Wind lidars are now used to detect the propagation of the wake trace (Archer et al., 2019) and, subsequently, measure both the mean (Iungo et al., 2013;Iungo and Porté-Agel, 2014;Menke et al., 2020) and the dynamic (Carbajo Fuertes et al., 2018) wake characteristics. Thus, they are used to evaluate the accuracy of different wake models (Trujillo et al., 2011;Trabucchi et al., 2017;Brugger et al., 2022). ...
We investigate the characteristics of the inflow and the wake of a 6MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions, with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the surge and sway motion of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterised in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals 8.5–9.5 ms-1 and 12.5–13.5 ms-1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %–6.4 %. The wake flow is measured by a wind lidar scanning in a horizontal plan position indicator mode, which reaches ten rotor diameters downwind. This study focuses on the downstream area between 3 and 8 rotor diameters. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit, that follows a Gaussian distribution. We find that even small variations (∼1 %–2 %) of the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine.
... Wind turbines operate in the atmospheric boundary layer (ABL), and the wake growth rate is influenced by terrain, surface roughness, atmospheric stability, and the operating regime of wind turbines, of which atmospheric stability is the most important influence factor and has been extensively investigated. Numerous field observations, [12][13][14][15][16] wind tunnel experiments, [17][18][19] and numerical simulations [20][21][22][23][24] have shown that atmospheric stability can significantly affect the evolution of wind-turbine wakes and the influence mechanisms can be summarized as follows: (1) For a specific surface roughness, atmospheric stability will influence the wake recovery by altering the turbulence production. In the convective boundary layer (CBL), the buoyancy effect contributes to the turbulence production, leading to higher turbulence intensity, which intensifies the turbulent mixing in the wake region and accelerates wind-turbine wake recovery, while in the stable boundary layer (SBL), the buoyancy effect suppresses the turbulence production and finally leads to a slower wake recovery. ...
... Our numerical simulations reproduce the influence of atmospheric stability on wake recovery, which is supported by numerous studies. 13,14,16,20,21 It should be noted that in the V-CBL, CBL, and NBL-H cases, wake recovery is very fast, and velocity-deficit profiles do not conform to the Gaussian distribution after 10D downstream of the wind turbine. As a result, in the following analysis, we will only focus on the wake characteristics before 10D downstream of the wind turbine in these cases, which is larger than the streamwise spacing 7$ 8D commonly used in the commercial wind farms. ...
The wind‐turbine wake growth is crucial for wake assessment. At present, it can only be determined empirically, which is the primary source of prediction errors in the analytical wake model, and a physically‐based method is urgently needed. Recently, the plume model is proposed for wake width prediction in the neutral boundary layer based on Taylor's diffusion theory. However, this model is not applicable for high‐roughness neutral and strongly convective conditions, which is mainly related to the fact that the specified far wake point in the plume model is too close to the virtual wake origin. In this condition, the wake width prediction has evident convex function characteristics, which is inconsistent with the actual linear expansion of wake width. To this end, we propose a coupled model of the plume model and the traditional wake model (CPT model) to calculate the wake growth rate iteratively, thereby obtaining the wake width and velocity deficits in the far‐wake region. The average wake width prediction error decreases from 11.75% to 3.1% in these conditions. Since the wind‐turbine‐induced‐turbulence contribution is dominant in the wake recovery in the very stable boundary layer, both models have low but engineering acceptable prediction accuracy. Except for the above conditions, both the plume and CPT models can predict the wake width well, and their average wake width prediction errors are 2.5% and 1.9%, respectively. This implies that the proposed CPT model has higher prediction accuracy and a broader application range.
... Significant power losses [2,13,42] and enhanced fatigue loads [7,10] were documented for turbines impinged by upstream wakes. The study of turbine wakes through numerical simulations is encumbered with difficulties due to the high Reynolds number flow (which entails a great span of length and time scales involved) [30], the unsteadiness of the inflow conditions [19,23], and the relevant role of atmospheric stability [51]. Recently, largeeddy simulations (LES) have become a well-established tool for the simulation of wind farm flows [5,28,40]. ...
... The above-mentioned challenges have spurred the interest of wind energy scientists in the experimental characterization of the wind farm flow. In particular, the improvements of remote sensing instruments, such as wind light detection and ranging (LiDAR), have promoted the proliferation of field experimental campaign investigating the wakes of utility-scale wind turbines (e.g., see [19,24,50]). These studies have highlighted the great complexity and sensitivity to environmental conditions on the characteristics and downstream evolution of wind turbine wakes. ...
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
... We use the measurements at the 18 m sonic anemometer to derive the stability within each 10 min period. As shown in Conti et al. (2020a), the sonic measurements at 18 m provide the best fit to the polynomial form of Högström (1988), which ...
... The lidarobserved maximum deficit varies between 30 % and 60 % within the first five rotor diameters, depending on the inflow turbulence conditions. Similar behaviours were reported in recent lidar measurement campaigns (Iungo and Porté-Agel, 2014;Machefaux et al., 2016;Fuertes et al., 2018;Zhan et al., 2020b). ...
We study the calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, USA. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence, and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds, and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows for both model implementation and uncertainty assessment. We validate the resulting fully resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far-wake region beyond four rotor diameters as long as properly calibrated parameters are used, and wake meandering time series are accurately replicated. We show that the current DWM model parameters in the IEC standard lead to conservative wake deficit predictions for ambient turbulence intensities above 12 % at the SWiFT site. Finally, we provide practical recommendations for reliable calibration procedures.
... Significant power losses [4, 5, 6] and enhanced fatigue loads [7,8] were documented for turbines impinged by upstream wakes. The study of turbine wakes through numerical simulations is encumbered with difficulties due to the high Reynolds number flow (which entails a great span of length and time scales involved) [3], the unsteadiness of the inflow conditions [9,10], and the relevant role of atmospheric stability [11]. Recently, large-eddy simulations (LES) have become a well-established tool for the simulation of wind farm flows [12,13,14]. ...
... The above-mentioned challenges have spurred the interest of wind energy scientists in the experimental characterization of the wind farm flow. In particular, the improvements of remote sensing instruments, such as wind Light Detection and Ranging (LiDAR), have promoted the proliferation of field experimental campaign investigating the wakes of utility-scale wind turbines (e.g., see [9,21,20]). These studies have highlighted the great complexity and sensitivity to environmental conditions on the characteristics and downstream evolution of wind turbine wakes. ...
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models that capture the wake flow-field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional \emph{latent} space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow-fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
... This interpolation can be a source of error , especially if a linear interpolation method is used (Garcia et al., 2017;Carbajo Fuertes et al., 2018;Beck and Kühn, 2017;Astrup et al., 2017). Delaunay triangulation has also been widely adopted for coordinate transformation (Clive et al., 2011;Trujillo et al., 2011Trujillo et al., , 2016Iungo and Porté-Agel, 2014;Machefaux et al., 2015), yet the accuracy has not been quantified in the case of nonuniformly distributed data. It is reasonable to weight the influence of the experimental points on their statistics according to the distance from the respective grid centroid, such as using uniform ), hyperbolic (Van Dooren et al., 2016, or Gaussian weights (Newsom et al., 2014;Wang and Barthelmie, 2015;Zhan et al., 2019). ...
... For the sake of completeness, LiSBOA is compared with other widely used techniques for statistical postprocessing of wind lidar data, specifically the Delaunay triangulation (see, e.g., Clive et al., 2011;Trujillo et al., 2011Trujillo et al., , 2016Iungo and Porté-Agel, 2014;Machefaux et al., 2015), linear interpolation in spherical coordinates (see, e.g., Mohr and Vaughan, 1979;Fuertes Carbajo and Porté-Agel, 2018), and window averaging (see, e.g., Newsom et al., 2008). Figure 20 shows the mean velocity and turbulence intensity fields retrieved from the considered LES data set through the various techniques for a step-stare virtual lidar scan. ...
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of the velocity statistical moments is proposed. LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional space, and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning lidars. LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for the optimal design of lidar experiments and efficient application of LiSBOA for the postprocessing of lidar measurements. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the lidar scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme. LiSBOA is assessed against a numerical data set generated using the virtual lidar technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind lidar are retrieved through LiSBOA, and then the estimated statistics are compared with those of the original LES data set, showing a maximum error of about 4 % for both mean velocity and turbulence intensity.
... [7][8][9][10] Regions of downwind, decreased wind speed (ws) and increased turbulence due to wind turbines are known as turbine wakes, the evolution of which is highly dependent on the stability of the ABL. 11,12 There has also been headway in numerical modeling of wind farms to characterize wake effects on the ABL and its associated features. [13][14][15][16][17][18] Despite these advances, there is still much to be understood about wind farm impacts on the ABL, especially from an observational basis. ...
The American WAKE ExperimeNt (AWAKEN) collaboration is an observational-based field campaign in northern Oklahoma intended to analyze the potential influence of onshore wind farms and their collective wakes on wind power production, turbine structural loads, and on the atmospheric boundary layer (ABL). Focusing on the ABL effects, the University of Oklahoma and the Lawrence Livermore National Laboratory collected continuous high-resolution kinematic and thermodynamic profile measurements during 2022 and Summer 2023. The deployment strategy for these campaigns is detailed first, followed by an initial comparison of data from two sites in the AWAKEN domain: a near-farm site to examine collective wake impacts on the ABL, and a far-field site remaining outside the wind farm-waked region. We summarize the datasets available and demonstrate the benefits of these observations and multiple value-added products (VAPs) for investigation of ABL features observed during AWAKEN. We also highlight examples of preliminary analyses, including ABL height detection and nocturnal low-level jet examination, which are produced using novel VAPs based on optimal estimation to retrieve deeper Doppler lidar wind profiles than previously resolved, along with their uncertainty. By including the near-farm and far-field site in these analyses, we identified a pattern of stronger lower-atmospheric mixing at the near-farm site than the far-field site, motivating deeper investigation into the relationship between wind farms and general ABL characteristics. Future analysis will delve deeper into this relationship by examining other ABL characteristics, such as atmospheric stability and convection.
... 29,131,132 Although several mechanisms of wake dynamics are well-understood in controlled environmental conditions, only the recent advent of remote sensing technology has permitted the characterization of wakes from utility-scale generators placed in the ABL. [40][41][42][133][134][135][136][137][138][139][140] The cited studies generally focus on the characterization of wakes from a single turbine under undisturbed inflow, whereas there is a need to further investigate the behavior of internal wind farm wakes. The AWAKEN instruments have the capability to detect single and merging wakes, thus potentially expanding the knowledge of previous field studies. ...
The American WAKE ExperimeNt (AWAKEN) is a multi-institutional field campaign focused on gathering critical observations of wind farm–atmosphere interactions. These interactions are responsible for a large portion of the uncertainty in wind plant modeling tools that are used to represent wind plant performance both prior to construction and during operation and can negatively impact wind energy profitability. The AWAKEN field campaign will provide data for validation, ultimately improving modeling and lowering these uncertainties. The field campaign is designed to address seven testable hypotheses through the analysis of the observations collected by numerous instruments at 13 ground-based locations and on five wind turbines. The location of the field campaign in Northern Oklahoma was chosen to leverage existing observational facilities operated by the U.S. Department of Energy Atmospheric Radiation Measurement program in close proximity to five operating wind plants. The vast majority of the observations from the experiment are publicly available to researchers and industry members worldwide, which the authors hope will advance the state of the science for wind plants and lead to lower cost and increased reliability of wind energy systems.
... operations of utility-scale wind turbines through a scanning Doppler LiDAR. [46][47][48] Specifically, we use the wake velocity profiles reported in Iungo et al, 49 where the most representative realizations of the wakes measured with a scanning Doppler LiDAR were clustered with a K-means algorithm. From the mentioned data set, the wake velocity statistics at a downwind distance of 1D from the turbine rotor are used as a reference to design the radial distribution of the porosity/solidity of the porous disks. ...
Neglecting the velocity reduction in the induction zone of wind turbines can lead to overestimates of power production predictions, and, thus, of the annual energy production for a wind farm. An experimental study on the induction zone associated with wind turbine operations is performed in the boundary‐layer test section of the BLAST wind tunnel at UT Dallas using stereo particle image velocimetry. This experiment provides a detailed quantification of the wind speed decrease associated with the induction zone for two different incoming flows, namely, uniform flow and boundary layer flow. Operations of wind turbines in different regions of the power curve are modeled in the wind tunnel environment with two porous disks with a solidity of 50.4% and 32.3%, which correspond to thrust coefficients of 0.71 and 0.63, respectively. The porous disks are designed to approximate the wake velocity profiles previously measured for utility‐scale wind turbines through scanning wind LiDARs. The results show that the streamwise velocity at one rotor diameter upwind of both disks decreases 1% more for the boundary layer flow than for the uniform flow. Further, the effect of shear in front of the disk with a higher thrust coefficient can be observed until 1.75 rotor diameter upwind of the disk, whereas for the disk with a lower thrust coefficient, the effect of shear becomes negligible at 1.25 rotor diameter upwind. It is found that at one rotor diameter upwind, for both incoming flows, the disk having a higher thrust coefficient has 2% more velocity reduction than the lower‐thrust‐coefficient disk. The results suggest that the variability in wind shear and rotor thrust coefficient, which is encountered during typical operations of wind turbines, should be considered for the development of improved models for predictions of the rotor induction zone, the respective cumulative effects in the presence of multiple turbines, namely, wind farm blockage, and more accurate predictions of wind farm power capture.
... The velocity profiles collected from PL04 are classified through the local time of the day, hub-height wind speed, wind direction, shear, veer, and LiDAR velocity dispersion. The shear is quantified here through the fitting of the shear exponent, α, on each velocity profile [28][29][30] : ...
Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ≈3%), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.
... This behavior is strongly typical of wind turbine wakes. 11,12,14 To Figure 13 plots the results, as well as the RANS NMAE minus the RF NMAE, given as ΔNMAE. ...
The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.
... A power law correlating dynamic solidity σ D and the absolute minimum velocity was determined by Araya et al. [23], indicating that an increasing σ D yields a faster rate or recovery of the velocity deficit into the wake, i.e., the higher the solidity, the greater the initial velocity deficit. The velocity recovery profiles of vertical-axis rotors were aligned to that of a solid cylinder according to the predictions of Schlichting [31] and Iungo and Porté-Agel [32] for turbulent free-shear flows and horizontal-axis wind turbine wakes. Since the maximum Reynolds stress was found on the spanwise plane, it is reasonable to assume that the turbine aspect ratio AR plays a significant role in the wake distribution. ...
The design of hydrokinetic plants in hydropower canals involves the choice of the array layout, rotor geometry, turbine spacing, and array spacing, and necessitates the control of the resultant backwater to avoid upstream flooding hazards. Several works in the literature have shown that array power optimization is feasible with small spacings between the arrays, disregarding the limitation in the power output induced by backwater upstream. In this study, a 1-D channel model with a Double Multiple Streamtube code and wake sub-models are integrated to predict an array layout that will maximize the array power. The outputs of the conducted sensitivity analysis confirm that this design enabled improved power conversion with closely spaced turbines and largely spaced arrays, thus allowing for a partial recovery of the total head variation for a new array deployed upstream. In addition to the quantitative assessment of the mechanical power converted, the tool enables depth control from the downstream undisturbed flow station to the backwater exhaustion far upstream, thereby increasing its flexibility. Furthermore, it overcomes the limitations of actuator disc models by considering rotor's fluid dynamic losses. The results show that power output linearly scales for a limited number of arrays (≤5), whilst the variation in water depth variation follows a power law from the most downstream array towards upstream, regardless of the plant size. Finally, the maximum upstream inflow depth is demonstrated to become asymptotic for large multi-array plants under ideal conditions.
... The effects of atmospheric stability on wake propagation characteristics and wind turbine performance have been widely studied in wind tunnel measurements [6][7][8][9] and field experiments [10][11][12][13]. ...
A validation study is carried out for a generalized actuator disk model (GAD) implemented into the Weather Research and Forecasting model, an open-source numerical weather prediction code, in order to simulate the aerodynamic behavior of a real-scale wind turbine under varying atmospheric conditions. Multiple large-eddy simulations (LESs) are performed to resolve energy-containing eddies of turbulent motions utilizing the GAD model, which calculates the wind turbine-induced forces distributed over the rotor disk. The benchmarks defined at the Scaled Wind Technology Facility (SWiFT) campaign, (see Ref. Doubrawa et al. (2020) for details), were chosen to validate the performance of the GAD model in terms of its capability to reproduce the wake and aerodynamic loading on the rotor. Meteorological data are available from a 60 m meteorological tower located 65 m upstream of the wind turbine, and the aerodynamic data, including scans of downstream velocity profiles, are available for the Vestas V27 wind turbine thanks to DTU’s nacelle-mounted, rear-facing SpinnerLidar (Mikkelsen et al., 2013). Rotor performance and wake recovery results obtained from the GAD model are compared with field experiments and other LES data.
... In contrast, scanning wind lidars are cheaper, very flexible in terms of the scan set-up and the installation (for instance, on a wind turbine transition piece), and easily accessible for system maintenance during the maintenance routines of wind farms. In the past, most studies, using scanning Doppler lidar, have been limited to investigations of the spatial wake characteristics of isolated wind turbines (Wang and Barthelmie, 2015;Bastine et al., 2015;Bingöl et al., 2010;Käsler et al., 2010) or individual wind farms (Smalikho et al., 2013;Aitken et al., 2014;Iungo and Porté-Agel, 2014;Herges et al., 2017;Krishnamurthy et al., 2017;Zhan et al., 2020), such as the velocity deficit, the single wake extent (length and width) of a wake, and wake meandering (Trujillo et al., 2010;Krishnamurthy et al., 2017) under various atmospheric conditions. More recently, lidars have also been used to study the wind speed reduction upstream of a wind farm, the so-called blockage effect (Schneemann et al., 2021). ...
As part of the ongoing X-Wakes research project, a 5-month wake-measurement campaign was conducted using a scanning lidar installed amongst a cluster of offshore wind farms in the German Bight. The main objectives of this study are (1) to demonstrate the performance of such a system and thus quantify cluster wake effects reliably and (2) to obtain experimental data to validate the cluster wake effect simulated by the flow models involved in the project. Due to the lack of free wind flow for the wake flow directions, wind speeds obtained from a mesoscale model (without any wind farm parameterization) for the same time period were used as a reference to estimate the wind speed deficit caused by the wind farm wakes under different wind directions and atmospheric stabilities. For wind farm waked wind directions, the lidar data show that the wind speed is reduced up to 30 % at a wind speed of about 10 m s-1, depending on atmospheric stability and distance to the wind farm. For illustrating the spatial extent of cluster wakes, an airborne dataset obtained during the scanning wind lidar campaign is used and compared with the mesoscale model with wind farm parameterization and the scanning lidar. A comparison with the results of the model with a wind farm parameterization and the scanning lidar data reveals a relatively good agreement in neutral and unstable conditions (within about 2 % for the wind speed), whereas in stable conditions the largest discrepancies between the model and measurements are found. The comparative multi-sensor and model approach proves to be an efficient way to analyze the complex flow situation in a modern offshore wind cluster, where phenomena at different length scales and timescales need to be addressed.
... Conversely, flow at a far enough distance from the wind turbine is independent of the turbine local characteristics, and instead only depends on some bulk properties of the turbine such as its thrust coefficient. its existence (Ainslie, 1985;Medici and Alfredsson, 2006;Troldborg et al., 2010;Iungo and Porté-Agel, 2014). ...
This thesis work presents experimental results on the effects of different ambient turbulence conditions on the performance and wake development of a model-scale wind turbine, focusing on two main parameters to characterise the nature of the incoming nature: these are its intensity I∞ and its spectral distribution, represented by the integral time scale of the free-stream T0. The power generated by the model-scale turbine is seen to increase with the intensity of the velocity fluctuations, as well as with the free-stream integral time scale; the former finding is in line with established literature, albeit the magnitude of the increase is higher than what expected, while the latter confirms some recent works that model the turbines as low-pass filters, more apt to harvest energy from low-frequency fluctuations. The wake evolution under turbulence is also observed to be highly affected by the atmospheric conditions: wakes developed under higher turbulence intensity evolve more rapidly under the condition that the free-stream T0 is low; in other words, for the same I∞, conditions that are favourable for power harvesting generate longer wakes, unfavourable from the point of view of a wind farm. The predictions of some analytical wake models are compared to the obtained wake, highlighting how fine-tuning of the model parameters can result in very accurate predictions. The results obtained stress the need to include a virtual origin in the wake models, a practice customary for bluff-bodies but seldom employed for wind turbines; this quantity has been seen to relate to the stability of the helical tip-vortex structure, and thus conveys a physical meaning. The rate at which the mean velocity field evolves in the streamwise direction has been related to the Reynolds shear stress in the turbine wake by means of an analytical relation; this allows to predict the mean velocity field in the turbine wake with knowledge limited to the second-order statistics. This last finding has been leveraged to formulate a framework using proper orthogonal decomposition to predict the full-scale wake from limited probe data; preliminary results show that this is possible with acceptable results, correctly predicting both the intensity of the turbulence in the turbine wake from limited data and the mean wake velocity.
... They can be either scanning downstream of the turbine where they are mounted [16,41] or looking upstream to the wake of the upstream turbine. When studying multiple wind turbine wakes or wind farm wakes, scanning lidars are probably the preferred solution [44], due, mainly, to the extension of wakes (up to tens of kilometers in some cases). For the latter case, one needs to use more than one scanning lidar to reduce the error and the amount of assumptions for the retrieval of the velocity components. ...
The 15 Early Stage Researchers (ESRs) in the LIKE project investigate topics in which wind lidar plays a significant role. In work package 5 Wind Energy of the project, the ESRs 8-12 investigates the use of lidar for different applications in wind energy. This report describes the state-of-the-art of applications and possible contributions in terms of innovation and impact of lidar technology. Each ESR provides a detailed description of their research topic and highlights the lidar's potential to improve the design of the selected application or to enhance its operation.
... Wind turbine wake behavior under different stabilities and for different wind speeds has been studied for both onshore (e.g., Iungo and Porté-Agel, 2014;Zhan et al., 2020) and offshore wind farms (e.g., Platis et al., 2022), but more research is needed to accurately describe the wind resource within wind parks and how power production can be optimized using wake steering. The extent of the wake behind a wind turbine is likely to be very sensitive to LLJ conditions (e.g., Vollmer et al., 2017), as the potential to mix down momentum from above the jet core is greatly diminished. ...
Non-idealized wind profiles frequently occur over the Baltic Sea and are important to take into consideration for offshore wind power, as they affect not only the power production but also the loads on the structure and the behavior of the wake behind the turbine. In this observational study, we classified non-idealized profiles as the following wind profiles having negative shear in at least one part of the lidar wind profile between 28 and 300 m: low-level jets (with a local wind maximum in the profile), profiles with a local minimum and negative profiles. Using observations spanning over 3 years, we show that these non-idealized profiles are common over the Baltic Sea in late spring and summer, with a peak of 40 % relative occurrence in May. Negative profiles (in the 28–300 m layer) mostly occurred during unstable conditions, in contrast to low-level jets that primarily occurred in stable stratification. There were indications that the strong shear zone of low-level jets could cause a relative suppression of the variance for large turbulent eddies compared to the peak of the velocity spectra, in the layer below the jet core. Swell conditions were found to be favorable for the occurrence of negative profiles and profiles with a local minimum, as the waves fed energy into the surface layer, resulting in an increase in the wind speed from below.
... New remote-sensing measurements are becoming available that will serve as validation data sets for the near future. New observations with scanning lidars (Bingöl, Mann, and Larsen 2010;Käsler et al. 2010;Trujillo et al. 2011;Aitken et al. 2014b;Iungo and Porté-Agel 2014;Aitken and Lundquist 2014;Banta et al. 2015) are proving valuable; for example, Sandia National Laboratories have the SWiFT facility, and researchers there have performed detailed observations of a single turbine wake using a high-resolution scanning lidar (Herges et al. 2017). Remote-sensing techniques will continue to be important as turbines grow in size, and more traditional meteorological towers will become cost prohibitive at larger heights. ...
The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different operational and atmospheric conditions and validate the use of different wind plant control strategies and simulation frameworks. An extensive field campaign like AWAKEN requires proper coordination and long-term planning to be successful. In this paper, we review the major activities planned during AWAKEN to provide information for current and future project partners. Specifically, we provide information about the project sites, their planned instruments, and how these will relate to the scientific objectives of the overall AWAKEN project.
... 10 Nonetheless, a recent analysis of data covering ten years of operations for the Lillgrund offshore wind farm (a power plant designed for power maximization) revealed a reduction in power capture as high as 28% of the nominal capacity. 11 The prediction of wake impact on wind farm performance is complicated by the influence of the atmospheric stability on wake recovery, [12][13][14][15] which results in significantly reduced power losses for high turbulence intensity and convective atmospheric conditions compared to low turbulence intensity, stable atmospheric conditions. 7,16,17 An additional hurdle for predicting wake-induced power losses is represented by the coalescence of multiple wakes, which creates a spatially heterogeneous and highly turbulent flow within the wind farm boundary layer. ...
Next-generation models of wind farm flows are increasingly needed to assist the design, operation, and performance diagnostic of modern wind power plants. Accuracy in the descriptions of the wind farm aerodynamics, including the effects of atmospheric stability, coalescing wakes, and the pressure field induced by the turbine rotors, and low computational costs are necessary attributes for such tools. The Pseudo-2D RANS model is formulated to provide an efficient solution of the Navier-Stokes equations governing wind-farm flows installed in flat terrain and offshore. The turbulence closure and actuator disk model are calibrated based on wind LiDAR measurements of wind turbine wakes collected under different operative and atmospheric conditions. A shallow-water formulation is implemented to achieve a converged solution for the velocity and pressure fields across a farm with computational costs comparable to those of mid-fidelity engineering wake models. The theoretical foundations and numerical scheme of the Pseudo-2D RANS model are provided, together with a detailed description of the verification and validation processes. The model is assessed against a large dataset of power production for an onshore wind farm located in North Texas showing a normalized mean absolute error of 5.6\% on the 10-minute-averaged active power and 3\% on the clustered wind farm efficiency, which represent 8\% and 24\%, respectively, improvements with respect to the best-performing engineering wake model tested in this work.
... While the overall wind turbine performance depends on the interaction between these effects, the influence of stability on wake recovery is more clear. Wakes recover faster in convective ABL conditions compared to stable or neutral conditions (Iungo and Porté-Agel, 2014), and relatedly, the wake meandering is enhanced (Abkar and Porté-Agel, 2015). Provided slower wake recovery as a function of streamwise distance downwind of a wind turbine in stable ABL conditions, wake interactions are generally increased (Abkar et al., 2016). ...
The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, in which variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer (ABL) flow features. The closed-loop wake steering control methodology developed in Part 1 (, 10.5194/wes-5-1315-2020) is implemented in an example eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw-aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. In our diurnal cycle simulations of a representative wind farm geometry, closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw-aligned control and open-loop lookup table control. The increase in energy production for the simulated wind farm design for closed- and open-loop wake steering control, compared to baseline yaw-aligned control, is 4.0 %–4.1 % and 3.4 %–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions in our present diurnal cycle simulations. Open-loop lookup table control decreases energy production in the example wind farm in the convective ABL conditions simulated, compared to baseline yaw-aligned control, while closed-loop control increases energy production in the convective conditions simulated.
... The analysis of wind farm cluster wake interaction is a complex task, as different interacting processes on multiple scales In the past, most studies, using scanning Doppler lidar, have been limited to investigations of the spatial wake characteristics of isolated wind turbines (Wang and Barthelmie, 2015;Bastine et al., 2015;Bingöl et al., 2010;Käsler et al., 2010) or individual wind farms (Smalikho et al., 2013;Aitken et al., 2014;Iungo and Porté-Agel, 2014;Herges et al., 2017;Krishnamurthy et al., 2017;Zhan et al., 2020), such as the velocity deficit, the single wake extent (length and width) of a wake, and wake meandering (Trujillo et al., 2010;Krishnamurthy et al., 2017) under various atmospheric conditions. More recently, lidars have also been 65 used to study the wind speed reduction upstream of a wind farm, the so-called blockage effect . ...
To establish long-term flow measurements for the validation of wake models, a scanning Doppler wind lidar system was installed at the western edge of the wind farm Gode Wind 1 in the German Bight for a period of five months. The main goal was to detect the wakes from clusters for different wind directions and atmospheric stabilities. The lidar data are categorized into five sectors based on the different upstream conditions. The influence of wakes and atmospheric stability are initially investigated with respect to airborne measurements collected within the lidar measurement period. Mesoscale simulations are used as a reference for the free wind flow. The percent wind speed difference downstream of the wind farm clusters and at the location of the scanning lidar measurements (1.5 km downstream the closest wind farm) can reach a maximum of about 30 % for a mean wind speed of 10 m s−1 depending on the wind direction and under stable atmospheric conditions. A good agreement between mesoscale simulations (without any wind farm parameterization) and lidar measurements is found for undisturbed wind sectors and unstable and near-neutral atmospheric conditions. By taking into account the surrounding wind farms through a parameterization in the mesoscale simulations, the agreement of the model with the measurements is relatively good for unstable and near-neutral conditions, including sectors influenced by wind farm wakes. For stable conditions, however, the highest discrepancies between simulations and observations occur. Overall, the scanning lidar dataset can be used as a validation tool for wake model validations.
... Thus, the main effects on standalone turbines are due to the changes in mean shear and turbulence intensity of the incoming flow, associated with changes in thermal stability. Many findings on these topics have already been reported from wind tunnel experiments (Whale et al., 2000;Chamorro et al., 2010;Zhang et al., 2013;Hancock et al., 2014), field observations (Baker et al., 1984;Magnusson et al., 1999;Iungo et al., 2014;Aitken et al., 2014a;Machefaux et al., 2016;Abraham et al., 2019;Uchida et al., 2021), statistical modeling (Braunbehrens and Segakini, 2019), and numerical simulations (Wu et al., 2012;Churchfield et al., 2012;Keck et al., 2014;Aitken et al., 2014b;Mirocha et al., 2015;Abkar et al., 2015;Machefaux et al., 2016;Draper et al., 2018;Sedaghatizadeh et al., 2018;Uchida et al., 2020Uchida et al., , 2021Uchida, 2020). In the above studies, the effects of turbulent mixing on the wake recovery of a wind turbine are mainly discussed. ...
The wake characteristics of a utility-scale wind turbine under realistic atmospheric boundary layer conditions are affected by the continuously changing wind direction arriving at the wind turbine. In the present study, the effects of continuous changes in the incoming wind direction were studied for a wind turbine on flat terrain, with the objective of understanding the wake characteristics of the wind turbine. Thus, understanding the effects of continuously changing incoming wind direction on the wake characteristics of wind turbines over flat terrain is important in the design of wind farm layouts, including in the design of offshore wind power plants. For this purpose, a computational fluid dynamics (CFD) approach using large-eddy simulations (LES) was adopted in the present study. An in-house LES-solver based on the actuator line (AL) aerodynamics technique was constructed in order to successfully capture the wake structure behind the wind turbine. First, experimental investigations on both a blade-only wind turbine scale model and a full 3D wind turbine scale model (isolated wind turbine) were conducted for a fixed inlet wind condition, the latter including the nacelle and the tower. Through a detailed comparison of the wind tunnel experimental and numerical results, the prediction accuracy of the in-house LES-solver was verified and validated for fixed inlet wind conditions. On the basis of the validation results obtained, and using the full 3D wind turbine scale model, the effects of the continuously changing inlet wind conditions on the wake characteristics in the near- and far-wake regions were numerically investigated. In addition, the effects of the wind turbine nacelle and tower on the wake characteristics were also investigated. The numerical results show that the most significant impact of the effects of the continuously changing wind direction was the rapid recovery of the mean velocity deficits in the wind turbine wake region. Further, at the x = 10D position (D is the rotor diameter) downstream of the wind turbine, the non-dimensional streamwise mean velocity was 0.93, which nearly matches the approaching flow speed, under an optimal tip speed ratio of 4.0, compared to the fixed wind direction scenario.
... The prediction of wake impact on wind farm performance is complicated by the influence of the atmospheric stability on wake recovery [11,12,13,14], which results in significantly reduced power losses for high turbulence intensity, A crucial aspect of a RANS model is the modeling of the turbulent Reynolds stresses, aimed at capturing the complex role of atmospheric turbulence, blade-and wake-generated turbulence, and wake dynamics, such as wake meandering [81,82]. The vast majority of the RANS models adopt the linear turbulent eddy viscosity hypothesis [83], with just a few examples of Reynolds Stress transport [84,85] and non-linear eddy viscosity closures [86]. ...
Next-generation models of wind farm flows are increasingly needed to assist the design, operation, and performance diagnostic of modern wind power plants. Accuracy in the descriptions of the wind farm aerodynamics, including the effects of atmospheric stability, coalescing wakes, and the pressure field induced by the turbine rotors, and low computational costs are necessary attributes for such tools. The Pseudo-2D RANS model is formulated to provide an efficient solution of the Navier-Stokes equations governing wind-farm flows installed in flat terrain and offshore. The turbulence closure and actuator disk model are calibrated based on wind LiDAR measurements of wind turbine wakes collected under different operative and atmospheric conditions. A shallow-water formulation is implemented to achieve a converged solution for the velocity and pressure fields across a farm with computational costs comparable to those of mid-fidelity engineering wake models. The theoretical foundations and numerical scheme of the Pseudo-2D RANS model are provided, together with a detailed description of the verification and validation processes. The model is assessed against a large dataset of power production for an onshore wind farm located in North Texas showing a normalized mean absolute error of 5.6% on the 10-minute-averaged active power and 3% on the clustered wind farm efficiency, which represent 8% and 24%, respectively, improvements with respect to the best-performing engineering wake model tested in this work.
... Regarding analytical wake modelling, there are several models that are simple and computationally inexpensive, yet superior in capturing the physics when compared to empirical models (i.e., obtained by data fitting) such as Zhang et al. [29], Iungo and Port e-Agel [30], and Aitken et al. [31]. This is due to the fact that these analytical wake models such as Jensen [9], Ainslie [10], Larsen [11], Frandsen et al. [12], Bastankhah and Port e-Agel [13], Qian and Ishihara [14], and Blondel and Cathelain [15] are derived from flow governing equations; either mass conservation equation only or mass and momentum conservation equations together [5]. ...
This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.
... While the overall wind turbine performance depends on the interaction between these effects, the 60 influence of stability on wake recovery is more clear. Wakes recover faster in convective ABL conditions compared to stable or neutral (Iungo and Porté-Agel, 2014), and relatedly, the wake meandering is enhanced (Abkar and Porté-Agel, 2015). Provided slower wake recovery as a function of streamwise distance downwind of a wind turbine in stable ABL conditions, wake interactions are generally increased (Abkar et al., 2016). ...
The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, where variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer flow (ABL) features. The closed-loop wake steering control methodology developed in Part 1 (Howland et al., Wind Energy Science, 2020, 5, 1315–1338) is implemented in an eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set-points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. Closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw aligned control and open-loop lookup table control. The increase in wind farm energy production for closed- and open-loop wake steering control compared to baseline yaw aligned control, is 4.0–4.1 % and 3.4–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions. Open-loop lookup table control decreases energy production in the convective ABL conditions simulated, compared to baseline yaw aligned control, while closed-loop control increases energy production in convective conditions.
... This velocity deficit profile and wake growth law is then combined with a superposition law, which is used to capture wake interactions between upstream turbines in order to predict the mean velocity seen by each turbine in the array. There is a vast amount of literature proposing different velocity deficit representations that range from analytical functions [1][2][3][4][5] to experimental 6,7 or data-driven parameterizations. 8 A number of different superposition laws for the deficits [9][10][11][12] have also been proposed, with the most common ones being quadratic or linear superposition. ...
This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.
... A deeper understanding of the physics of turbine wakes was achieved by calculating temporal (Trujillo et al., 2011;Iungo et al., 2013b;Iungo and Porté-Agel, 2014;Kumer et al., 2015;Machefaux et al., 2015;Van Dooren et al., 2016) or conditional (Aubrun et al., 2016;Machefaux et al., 2016;Garcia et al., 2017;Bromm et al., 2018;Iungo et al., 2018;Zhan et al., 2019Zhan et al., , 2020 statistics of the velocity collected through lidar scans performed at different times. Using this approach, Iungo and Porté-Agel (2014) detected a significant dependence of the wake recovery rate on atmospheric stability, based on time-averaged volumetric lidar scans. ...
The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in , is a procedure for the optimal design of lidar scans and calculations over a Cartesian grid of the statistical moments of the velocity field. Lidar data collected during a field campaign conducted at a wind farm in complex terrain are analyzed through LiSBOA for two different tests. For both case studies, LiSBOA is leveraged for the optimization of the azimuthal step of the lidar and the retrieval of the mean equivalent velocity and turbulence intensity fields. In the first case, the wake velocity statistics of four utility-scale turbines are reconstructed on a 3D grid, showing LiSBOA's ability to capture complex flow features, such as high-speed jets around the nacelle and the wake turbulent-shear layers. For the second case, the statistics of the wakes generated by four interacting turbines are calculated over a 2D Cartesian grid and compared to the measurements provided by the nacelle-mounted anemometers. Maximum discrepancies, as low as 3 % for the mean velocity (with respect to the free stream velocity) and turbulence intensity (in absolute terms), endorse the application of LiSBOA for lidar-based wind resource assessment and diagnostic surveys for wind farms.
... Iungo and Porté-Agel, 2014;Machefaux et al., 2016;Fuertes et al., 2018;Zhan et al., 2020b). The lidar-estimated ...
In this first part of a two-part work, we study the calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, U.S.A. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows both for model implementation and uncertainty assessment. We validate the resulting fully-resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far wake region beyond four rotor diameters, as long as properly-calibrated parameters are used and wake meandering time series are accurately replicated. We demonstrate that the current DWM-model parameters in the IEC standard lead to conservative wake deficit predictions. Finally, we provide practical recommendations for reliable calibration procedures.
... This analysis shows that the clusters have been generated based on the intensity of the velocity deficit, recovery rate, and downstream extent of the wake. To provide a more quantitative analysis of the results obtained through the clustering procedure, for each cluster, the maximum velocity deficit, * , is estimated as a function of the downstream location, , as reported in Fig. 7. Subsequently, * is fitted through the following power law for / ≥ 3 [3,19] ...
Wind turbine wakes are responsible for power losses and added fatigue loads of wind turbines. Providing capabilities to predict accurately wind-turbine wakes for different atmospheric conditions and turbine settings with low computational requirements is crucial for the optimization of wind-farm layout, and for improving wind-turbine controls aiming to increase annual energy production (AEP) and reduce the levelized cost of energy (LCOE) for wind power plants. In this work, wake measurements collected with a scanning Doppler wind Li- DAR for broad ranges of the atmospheric static stability regime and incoming wind speed are processed through K-means clustering. For computational feasibility, the cluster analysis is performed on a low-dimensional embedding of the collected data, which is obtained through proper orthogonal decomposition (POD). After data compression, we perform K-means of the POD modes to identify cluster centers and corresponding members from the LiDAR data. The different cluster centers allow us to visualize wake variability over ranges of atmospheric, wind, and turbine parameters. The results show that accurate mapping of the wake variability can be achieved with K-means clustering, which represents an initial step to develop data-driven wake models for accurate and low-computational-cost simulations of wind farms.
Doppler LiDARs are considered as promising alternative to meteorological masts for wind resource assessments for wind energy application. The current study models a single scanning LiDAR-based wind field measurements in the LES and quantify the effect of scan parameters, i.e, measurement range, azimuth and elevation angles and wind direction on the accuracy of two-parameter velocity volume processing (VVP) method for computing velocity vectors from radial wind speeds. The mean wind speeds computed from LiDAR measurements show good agreement with the original LES data. The error increases with the measurement range, but it decreases with azimuth range, with θrange = 60° giving the most accurate mean wind speeds among the three azimuth range considered in this study. The wind direction did not particularly effect the accuracy of the mean wind speed estimation, though larger difference between wind direction and scan direction results in increased variation in the VVP fitting. The effect of elevation angle is investigated with lower elevation angle scan of 3.4°. Although stronger shear near the ground led to larger difference between the LiDAR and LES data, for higher points the effect of vertical shear on mean wind speeds is not significant. In terms of turbulence intensities, the two-parameter VVP significantly underestimates their values for all the case considered in this study. This is because a significant fraction of the fluctuating components is filtered out while fitting the data over the scan arc. The study therefore, proposes an improvement to the conventional VVP method, based on the Reynolds decomposition of wind speed components. Turbulence intensities estimated using this method show higher degree of variation, though the accuracy improved with increasing azimuth range.
Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale (𝒪(mm)) up to large energy-containing structures (𝒪(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system.
To maximize the profitability of wind power plants, wind farms are often characterized by high wind turbine density leading to operations with reduced turbine spacing. As a consequence, the overall wind farm power capture is hindered by complex flow features associated with flow modifications induced by the various wind turbine rotors. In addition to the generation of wakes, the velocity of the incoming wind field can reduce due to the increased pressure in the proximity of a single turbine rotor (named induction); a similar effect occurs at the wind-farm level (global blockage), which can have a noticeable impact on power production. On the other hand, intra-wind-farm regions featuring increased velocity compared to the freestream (speedups) have also been observed, which can be a source for a potential power boost. To quantify these rotor-induced effects on the incoming wind velocity field, three profiling LiDARs and one scanning wind LiDAR were deployed both before and after the construction of an onshore wind turbine array. The different wind conditions are classified according to the ambient turbulence intensity and streamwise/spanwise spacing among wind turbines. The analysis of the mean velocity field reveals enhanced induction and speedup under stably stratified atmospheric conditions. Furthermore, a reduced horizontal area between adjacent turbines has a small impact on the induction zone but increases significantly the speedup between adjacent rotors.
Over the last decades, pulsed light detection and ranging (LiDAR) anemometry has gained growing attention in probing the marine atmospheric boundary layer (MABL) due to its ease of use combined with compelling spatio‐temporal resolution. Among several scanning strategies, fixed scans represent the most prominent choice when high‐frequency resolution is required; however, no information is provided about the spatial heterogeneity of the wind field. On the other hand, volumetric scans allow for the characterization of the spatial variability of the wind field with much lower temporal resolution than fixed scans. In this work, the recently developed “LiDAR Statistical Barnes Objective Analysis” (LiSBOA) algorithm for the optimal design of LiDAR scans and retrieval of wind velocity statistics is tailored for applications in the MABL. The LiDAR data, collected during a recent experimental campaign over Lake Lavon in Texas, show a good consistency of mean velocity profiles between fixed and LiSBOA‐interpolated volumetric data, thus further encouraging the use of coupled fixed and volumetric scans for simultaneous characterizations of wind turbulence statistics along the vertical direction and volumetric heterogeneity of the wind field.
In this article, we provide a methodological framework for designing the scanning strategies of nacelle-mounted scanning lidars for wind energy field experiments, and apply it at two major experimental field campaigns. For the Rotor Aerodynamics, Aeroelastics, and Wake project (RAAW), we leverage two scanning lidars on one turbine to characterize the incoming turbulence and the turbine wake. For the American WAKE experimeNt (AWAKEN), we use four scanning lidars on top of four turbines in a large wind power plant to investigate both individual wakes and wind-plant-scale flow features.
The exponential growth of wind energy and the need to exploit wind resources over areas with higher energy potential have led to the construction of neighboring wind turbines and farms with relatively small separation distances. As a result, for specific wind and atmospheric conditions, the wakes generated by an upstream wind farm may affect wind resources available for a downstream wind farm resulting in detrimental impacts on energy harvesting and structural loads for the downwind wind turbines. Distances between neighboring wind farms are typically larger than those associated with intra-wind-farm wake interactions, generating cumulative wakes whose characteristics might differ from those predicted through classical engineering wake models. These phenomena are referred to as farm-to-farm interactions. A better understanding and characterization of farm-to-farm interactions is one of the science goals tackled by the ongoing American WAKE experimeNt (AWAKEN). The site under investigation for this field campaign comprises two large wind farms in northern Oklahoma, USA, which are spaced roughly 5km apart along the prevailing South-North wind direction. To investigate possible interactions between these two wind farms, the WindFluX mobile LiDAR station has been deployed mainly to perform volumetric scans over their gap region. In this paper, preliminary results from these LiDAR volumetric scans will be discussed, specifically for a case with multiple wind turbine wakes evolving during the occurrence of a low-level jet.
The wake characteristics of wind turbine mounted on complex terrains are of great significance in wind energy utilization. The ground roughness and atmospheric stratification are two key factors for this issue and their coupling effect has not been fully studied before. In this research, the wake distributions of wind turbine mounted on two typical complex terrains and a real complex terrain are investigated using large eddy simulations and actuator disk model with rotation. The ground roughness and atmospheric stratification are simulated by adding source term in the governing equations of fluid dynamics to generated temperature gradient and vegetation canopy. It is found that the effects of ground roughness and atmospheric stratification on the wake of wind turbine are superimposed or counteracted depending on the shape of complex terrain. The presence of atmospheric stratification reduces the power of wind turbine drastically by more than 40% and the rough ground causes a small decrease in the power of wind turbine on typical complex terrains. For the multiple wind turbines on a real terrain with rough ground, the influence of atmospheric stratification on the wake characteristics and power production of each wind turbine strongly affected by the micrositing of wind turbines. The usual practice in industry of multiplying the oncoming wind profile by the turbine power curve overestimates the power production of wind turbines on complex terrains with atmospheric stratification by up to 670% compared with the numerical results.
A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions.
LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables to identify of the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.
Using the random forest (RF) algorithm, this study presented a key parameter to characterize the mean wake of H-rotor VAWTs while modelling the wake. First, the RF algorithm was used to establish the regression relationship between the average wake velocity distribution and the rotor features. Next, the feature crosses method was combined with the RF algorithm to analyze the interaction and importance of the inputs. It was found that the normalized importance of a synthetic feature in wake modelling occupied a considerable significance, reaching 0.884 out of 1. The RF wake model with this parameter as the only input feature could successfully reconstruct the wake. It was found that this feature may reflect the ability of incident wind passing through the operating rotor and played a decisive role in the wake velocity distribution, including initial velocity deficit and wake recovery rate. The universality of this parameter was proved through cases analysis of wind turbines under different sizes and operating conditions, the. The study of the wake field is important for the modelling of the H-rotor VAWT wake field, and hence affects the optimal configuration of the wind farm.
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability.
Atmospheric stability can significantly influence the spreading of wind-turbine wakes. The previous studies often coupled atmospheric stability with the turbulence intensity and attributed the influence of atmospheric stability to the enhancement or suppression of turbulence due to the buoyancy effect. In this study, we decouple atmospheric stability with the ambient turbulence intensity, and the pure influence of atmospheric stability on the turbine wakes is investigated at a certain hub-height turbulence intensity via large-eddy simulation. We find that the spanwise turbulence transport plays a crucial role in wake recovery, and atmospheric stability influences this transport by redistributing the turbulence intensity between the three components and altering the spatial scales of the atmospheric motion. Under the convection condition, the spanwise turbulence intensity is greatly enhanced with enlarged flow scales. Hence, more Reynolds shear stress is generated under the shear effect between the ambient flow and the wake flow, which thus enhances spanwise turbulence transport, resulting in a faster recovery of turbine wakes. While for the stable condition, although the spanwise turbulence intensity is slightly enhanced, the flow scales are obviously reduced, resulting in a decrease of wake meandering, which leads to a decrease of turbulence transport in the wake region and a slower recovery of wind-turbine wakes.
Ces dernières décennies ont connu un fort développement de la demande en énergie éolienne, du fait de ses potentialités pour réduire les émissions de CO2 pour la production d’électricité. Dans ce contexte, il est nécessaire d’optimiser les stratégies de production d’énergie dans les parcs d’éoliennes. En effet ces derniers subissent des pertes de production et une augmentation de la fatigue structurelle des éoliennes est observée. Une des principales causes est liée aux interactions de sillages. Des solutions prometteuses pour atténuer ces effets sont les stratégies de contrôle d'induction ou de lacet appliquées aux éoliennes individuellement. Ce travail de recherche étudie la réponse dynamique d’un sillage d’une éolienne contrôlée en lacet et de son impact sur la charge exercée sur une éolienne en aval. Des scenarii de désalignement sont ainsi reproduits en souffleries avec des modèles d’éoliennes de type disque poreux. Les expérimentations sont réalisées à deux échelles réduites et pour deux types d’écoulements incidents : un turbulent homogène et isotrope, et une couche limite atmosphérique. Les transitoires rencontrés pendant les manœuvres en lacet sont analysés via des mesures de vélocimétrie laser par imagerie de particules et d’efforts par balance aérodynamique. Les principaux résultats montrent que les dynamiques du sillage et de la charge résultante sont indépendantes du type d’écoulement et du nombre de Reynolds, mais en revanche, elles dépendent du sens de la manœuvre (lacet croissant ou décroissant). En complément, l’interaction de sillage entre deux éoliennes à échelle réelle est également étudiée grâce au traitement de données acquises durant une campagne d’essais de terrain réalisée dans le cadre du projet SMARTEOLE. Grâce aux corrélations entre les séries temporelles de puissance générée, les déphasages entre les réponses des deux éoliennes sont quantifiés et l’influence du niveau d’interaction de sillage et de la vitesse de vent incident sont estimées.
An experimental study conducted in a wind tunnel on the mixing of moist air by a scaled wind turbine is presented. The experimental setup allows us to generate stable stratification conditions with respect to relative humidity and temperature in a closed-loop wind tunnel. The flow and its thermodynamic properties were characterized using a Cobra probe (a multi-hole pitot tube) and a sensor of local temperature and relative humidity, both used simultaneously to obtain vertical profiles. The flow and its stratification were measured downstream of a scaled rotor at two different streamwise distances (1 and 10 rotor diameters) and two Reynolds numbers based on the diameter of the wind turbine rotor (22 000 and 44 000, respectively). This was then compared to the inflow conditions. The wake mean structure and the humidity and temperature stratifications of the flow are found to be affected by the presence of the rotor. In particular, the stratification was always smaller one diameter downstream from the model (when compared to the empty test section case), and then was mostly recovered in the far wake (10 diameters downstream). This effect depended not only on the streamwise distance, but also on the Reynolds number of the flow. Finally, the bulk Richardson number Rb was found to be an appropriate parameter to quantify this effect.
A numerical study of atmospheric turbulence effects on wind-turbine wakes is presented. Large-eddy simulations of neutrally-stratified atmospheric boundary layer flows through stand-alone wind turbines were performed over homogeneous flat surfaces with four different aerodynamic roughness lengths. Emphasis is placed on the structure and characteristics of turbine wakes in the cases where the incident flows to the turbine have the same mean velocity at the hub height but different mean wind shears and turbulence intensity levels. The simulation results show that the different turbulence intensity levels of the incoming flow lead to considerable influence on the spatial distribution of the mean velocity deficit, turbulence intensity, and turbulent shear stress in the wake region. In particular, when the turbulence intensity level of the incoming flow is higher, the turbine-induced wake (velocity deficit) recovers faster, and the locations of the maximum turbulence intensity and turbulent stress are closer to the turbine. A detailed analysis of the turbulence kinetic energy budget in the wakes reveals also an important effect of the incoming flow turbulence level on the magnitude and spatial distribution of the shear production and transport terms.
Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output.
The observations indicate an initial velocity deficit of 50%−60% immediately behind the turbine, which gradually declines to 15%−25% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.
Field measurements of the wake flow produced from a 2-MW Enercon E-70 wind turbine were performed using three scanning Doppler wind lidars. A GPS-based technique was used to determine the position of the wind turbine and the wind lidar locations, as well as the direction of the laser beams. The lidars used in this study are characterized by a high spatial resolution of 18 m, which allows the detailed characterization of the wind turbine wake. Two-dimensional measurements of wind speed were carried out by scanning a single lidar over the vertical symmetry plane of the wake. The mean axial velocity field was then retrieved by averaging 2D scans performed consecutively. To investigate wake turbulence, single lidar measurements were performed by staring the laser beam at fixed directions and using the maximum sampling frequency. From these tests, peaks in the velocity variance are detected within the wake in correspondence of the turbine top tip height; this enhanced turbulence could represent a source of dangerous fatigue loads for downstream turbines. The spectral density of the measured velocity fluctuations shows a clear inertial-range scaling behavior. Then, simultaneous measurements with two lidars were performed in order to characterize both the axial and the vertical velocity components. For this setup, the two velocity components were retrieved only for measurement points for which the two laser beams crossed nearly at a right angle. Statistics were computed over the sample set for both velocity components, and they showed strong flow fluctuations in the near-wake region at turbine top tip height, with a turbulence intensity of about 30%.
The wake flow produced from an Enercon E-70 wind turbine is investigated
through three scanning Doppler wind LiDARs. One LiDAR is deployed upwind
to characterize the incoming wind, while the other two LiDARs are
located downstream to carry out wake measurements. The main challenge in
performing measurements of wind turbine wakes is represented by the
varying wind conditions, and by the consequent adjustments of the
turbine yaw angle needed to maximize power production. Consequently,
taking into account possible variations of the relative position between
the LiDAR measurement volume and wake location, different measuring
techniques were carried out in order to perform 2-D and 3-D
characterizations of the mean wake velocity field. However, larger
measurement volumes and higher spatial resolution require longer
sampling periods; thus, to investigate wake turbulence tests were also
performed by staring the LiDAR laser beam over fixed directions and with
the maximum sampling frequency. The characterization of the wake
recovery along the downwind direction is performed. Moreover, wake
turbulence peaks are detected at turbine top-tip height, which can
represent increased fatigue loads for downstream wind turbines within a
wind farm.
The wind industry in the United States has experienced a remarkably
rapid expansion of capacity in recent years and this fast growth is
expected to continue in the future. While converting wind's kinetic
energy into electricity, wind turbines modify surface-atmosphere
exchanges and the transfer of energy, momentum, mass and moisture within
the atmosphere. These changes, if spatially large enough, may have
noticeable impacts on local to regional weather and climate. Here we
present observational evidence for such impacts based on analyses of
satellite data for the period of 2003-2011 over a region in west-central
Texas, where four of the world's largest wind farms are located. Our
results show a significant warming trend of up to 0.72°C per decade,
particularly at night-time, over wind farms relative to nearby
non-wind-farm regions. We attribute this warming primarily to wind farms
as its spatial pattern and magnitude couples very well with the
geographic distribution of wind turbines.
Observations of wakes from individual wind turbines and a multi-megawatt wind energy installation in the Midwestern US indicate that directly downstream of a turbine (at a distance of 190 m, or 2.4 rotor diameters (D)), there is a clear impact on wind speed and turbulence intensity (TI) throughout the rotor swept area. However, at a downwind distance of 2.1 km (26 D downstream of the closest wind turbine) the wake of the whole wind farm is not evident. There is no significant reduction of hub-height wind speed or increase in TI especially during daytime. Thus, in high turbulence regimes even very large wind installations may have only a modest impact on downstream flow fields. No impact is observable in daytime vertical potential temperature gradients at downwind distances of >2 km, but at night the presence of the wind farm does significantly decrease the vertical gradients of potential temperature (though the profile remains stably stratified), largely by increasing the temperature at 2 m.
We examine the influence of a modern multi-megawatt wind turbine on wind and turbulence profiles three rotor diameters (
D
D
) downwind of the turbine. Light detection and ranging (lidar) wind-profile observations were collected during summer 2011 in an operating wind farm in central Iowa at 20-m vertical intervals from 40 to 220 m above the surface. After a calibration period during which two lidars were operated next to each other, one lidar was located approximately
2D
2
D
directly south of a wind turbine; the other lidar was moved approximately
3D
3
D
north of the same wind turbine. Data from the two lidars during southerly flow conditions enabled the simultaneous capture of inflow and wake conditions. The inflow wind and turbulence profiles exhibit strong variability with atmospheric stability: daytime profiles are well-mixed with little shear and strong turbulence, while nighttime profiles exhibit minimal turbulence and considerable shear across the rotor disk region and above. Consistent with the observations available from other studies and with wind-tunnel and large-eddy simulation studies, measurable reductions in wake wind-speeds occur at heights spanning the wind turbine rotor (43–117 m), and turbulent quantities increase in the wake. In generalizing these results as a function of inflow wind speed, we find the wind-speed deficit in the wake is largest at hub height or just above, and the maximum deficit occurs when wind speeds are below the rated speed for the turbine. Similarly, the maximum enhancement of turbulence kinetic energy and turbulence intensity occurs at hub height, although observations at the top of the rotor disk do not allow assessment of turbulence in that region. The wind shear below turbine hub height (quantified here with the power-law coefficient) is found to be a useful parameter to identify whether a downwind lidar observes turbine wake or free-flow conditions. These field observations provide data for validating turbine-wake models and wind-tunnel observations, and for guiding assessments of the impacts of wakes on surface turbulent fluxes or surface temperatures downwind of turbines.
There is an urgent need to develop and optimize tools for designing large wind farm arrays for deployment offshore. This research is focused on improving the understanding of, and modeling of, wind turbine wakes in order to make more accurate power output predictions for large offshore wind farms. Detailed data ensembles of power losses due to wakes at the large wind farms at Nysted and Horns Rev are presented and analyzed. Differences in turbine spacing (10.5 versus 7 rotor diameters) are not differentiable in wake-related power losses from the two wind farms. This is partly due to the high variability in the data despite careful data screening. A number of ensemble averages are simulated with a range of wind farm and computational fluid dynamics models and compared to observed wake losses. All models were able to capture wake width to some degree, and some models also captured the decrease of power output moving through the wind farm. Root-mean-square errors indicate a generally better model performance for higher wind speeds (10 rather than 6 m s(-1)) and for direct down the row flow than for oblique angles. Despite this progress, wake modeling of large wind farms is still subject to an unacceptably high degree of uncertainty.
The aerodynamics of horizontal axis wind turbine wakes is studied. The contents is directed towards the physics of power extraction by wind turbines and reviews both the near and the far wake region. For the near wake, the survey is restricted to uniform, steady and parallel flow conditions, thereby excluding wind shear, wind speed and rotor setting changes and yawed conditions. The emphasis is put on measurements in controlled conditions. For the far wake, the survey focusses on both single turbines and wind farm effects, and the experimental and numerical work are reviewed; the main interest is to study how the far wake decays downstream, in order to estimate the effect produced in downstream turbines. The article is further restricted to horizontal axis wind turbines and excludes all other types of turbines.
It is proposed that the ratios of the standard deviations of the horizontal velocity components to the friction velocity in the surface layer under convective conditions depend only onz
i
/L wherez
i
is the height of the lowest inversion andL is the Monin-Obukhov length. This hypothesis is tested by using observations from several data sets over uniform surfaces and appears to fit the data well. Empirical curves are fitted to the observations which have the property that at largez
i
/-L, the standard deviations become proportional tow
*, the convective scaling velocity.Fluctuations of vertical velocity obtained from the same experiments scale withz/L, wherez is the height above the surface, in good agreement with Monin-Obukhov theory.
The Hong Kong Observatory (HKO) operates a Doppler LIght Detection And Ranging (LIDAR) system at the Hong Kong International Airport (HKIA) to monitor the airflow around the airport area. The LIDAR measures the radial component of the wind field. To better visualize the airflow, a variational method which has been successfully applied to Doppler Radar data is adopted in this study to retrieve the two-dimensional wind field based on the LIDAR measurements. The wind field so obtained is found to reveal many salient features of terrain-induced airflow disturbances at HKIA, such as mountain waves and vortices. Its application to the detection of low-level windshear is demonstrated through selected cases. With a simple extension of the variational method, dual-Doppler analysis is also carried out using the radial velocity data from both the LIDAR and a Terminal Doppler Weather Radar (TDWR) to retrieve the wind field at the airport area in a gust front event.
German
Das Hong Kong Observatorium (HKO) betreibt am Internationalen Flughafen von Hong Kong (HKIA) ein Doppler-LIDAR-System zur Überwachung des Windfeldes im Bereich des Flughafens. Das LIDAR misst nur die radiale Komponente des Windfeldes. Für eine bessere Visualisierung der Strömung ist daher in dieser Studie erfolgreich eine Variationsmethode auf die Doppler-Radar-Daten angewendet worden, um das zweidimensionale Windfeld aus den LIDAR-Messungen abzuleiten. Das so erhaltene Windfeld enthält viele charakteristische Strukturen wie Schwerewellen und Wirbel, die auf Geländestrukturen in der Nähe von HKIA zurückzuführen sind. Die Anwendung der Methode zur Aufspürung von bodennahen Windscherungen wird an einigen Beispielen demonstriert. Über eine einfache Ausweitung der Variationsmethode wird auch eine Dual-Doppler-Analyse der Daten der radialen Windkomponenten aus den LIDAR- und Terminal Doppler-Wetterradar-Messungen ausgeführt, um das Windfeld in Flughafennähe beim Durchzug einer Böenfront zu erhalten.
The paper describes a convective boundary layer experiment conducted in
April 1978 at the Boulder Atmospheric Observatory, and examines the
spectral behavior of wind velocity and temperature from the
Observatory's 300 m tower, from aircraft flights alongside the tower and
from a surface network of anemometers, for evidence of terrain influence
on turbulence structure. The gently rolling terrain at the site does not
seem to affect the turbulence spectra from the tower in any perceptible
manner, except for minor shifts in the vertical velocity and temperature
spectral peaks. The aircraft vertical velocity spectra showed different
shapes for alongwind and crosswind sampling directions, as in earlier
measurements over ocean surfaces, and their peaks are displaced to
higher wavenumbers compared with the tower spectra. Long-term spectra of
horizontal wind components from surface stations around the tower
exhibit no particular sensitivity to site selection. Under
near-stationary conditions the peak of the spectrum of the streamwise
component tends to reflect more closely the predominant boundary layer.
convective scales than does the peak of the lateral wind component. The
problem of identifying those scales in the presence of large shifts in
wind direction is discussed.
We introduce a dynamical approach for the determination of power curves for wind
turbines and compare it with two common methods—among them the standard procedure
due to IEC 61400-12-1, i.e. the international standard prepared and published by the
International Electrotechnical Commission. The main idea of the new method is to separate
the dynamics of a wind turbine's power output into a deterministic and a stochastic part,
corresponding to the actual behaviour of the wind turbine and external influences such as
the turbulence of the wind, respectively. In particular, the governing coefficients are
reconstructed from the data, and the power characteristic is extracted as the stationary
states of the deterministic behaviour. Our results prove that a dynamical approach
enables one to grasp the actual conversion dynamics of a wind turbine and to gain
most accurate results for the power curve, independent of site-specific influences.
Dual-Doppler lidar observations are used to assess the accuracy of single-Doppler retrievals of microscale wind and temperature fields in a shear-driven convective boundary layer. The retrieval algorithm, which is based on four-dimensional variational data assimilation, is applied by using dual-and single-Doppler lidar data that are acquired during the Joint Urban 2003 field experiment. The velocity field that was retrieved using single-Doppler data is compared directly with radial velocities that were measured by a second noncollocated lidar. Dual-Doppler retrievals are also performed and then compared with the single-Doppler retrieval. The linear correlation coefficient and rms deviation between the single-Doppler retrieval and the observations from the second lidar are found to be 0.94 and 1.2 m s 1 , respectively. The high correlation is mainly the result of good agreement in the mean vertical structure as observed by the two lidars. Comparisons between the single-and dual-Doppler retrieval indicate that the single-Doppler re-trieval underestimates the magnitude of fluctuations in the crossbeam direction. Vertical profiles of hori-zontally averaged correlations between the single-and dual-Doppler retrievals also show a marginal cor-relation (0.4–0.8) between one of the horizontal velocity components. Again, this suggests that the retrieval algorithm has difficulty estimating the crossbeam component from single-Doppler data.
As the average hub height and blade diameter of new wind turbine installations continue to increase, turbines typically encounter higher wind speeds, which enable them to extract large amounts of energy, but they also face challenges due to the complex nature of wind flow and turbulence in the planetary boundary layer (PBL). Wind speed and turbulence can vary greatly across a turbine's rotor disk; this variability is partially due to whether the PBL is stable, neutral or convective. To assess the influence of stability on these wind characteristics, we utilize a unique data set including observations from two meteorological towers, a surface flux tower and high-resolution remote-sensing sound detection and ranging (SODAR) instrument. We compare several approaches to defining atmospheric stability to the Obukhov length (L). Typical wind farm observations only allow for the calculation of a wind shear exponent (α) or horizontal turbulence intensity (IU) from cup anemometers, whereas SODAR gives measurements at multiple heights in the rotor disk of turbulence intensity (I) in the latitudinal (Iu), longitudinal (Iv) and vertical (Iw) directions and turbulence kinetic energy (TKE). Two methods for calculating horizontal Ifrom SODAR data are discussed. SODAR stability parameters are in high agreement with the more physically robust L,with TKE exhibiting the best agreement, and show promise for accurate characterizations of stability. Vertical profiles of wind speed and turbulence, which likely affect turbine power performance, are highly correlated with stability regime. At this wind farm, disregarding stability leads to over-assessments of the wind resource during convective conditions and under-assessments during stable conditions. Copyright © 2011 John Wiley & Sons, Ltd.
Data from ther new Danish wind energy test site at Høvsøre is used to illustrate similarities and differences between wind meteorology within the Atmospheric Boundary Layer and Atmospheric Surface layer
Long-range Doppler wind light detection and ranging (lidar) measurements at a wind turbine were carried
out for the first time. The turbine was of the type Areva M5000 and is located at a site near the coastline in Bremerhaven, in the northern part of Germany. This wind turbine is the prototype for the German offshore test site ‘‘alpha ventus’’ and has a rated power of 5 MW. Information about the ambient wind field before and after this multimegawatt wind turbine was obtained. In this paper the measurement technique is discussed and the results of measurements in the diurnal layer and in the stable nocturnal boundary layer are shown. The main focus of this work is to determine the reduction of the wind speed at certain distances downstream from the rotor.
Large-eddy simulation (LES), coupled with a wind-turbine model, is used to investigate the characteristics of a wind-turbine
wake in a neutral turbulent boundary-layer flow. The tuning-free Lagrangian scale-dependent dynamic subgrid-scale (SGS) model
is used for the parametrisation of the SGS stresses. The turbine-induced forces (e.g., thrust, lift and drag) are parametrised
using two models: (a) the ‘standard’ actuator-disk model (ADM-NR), which calculates only the thrust force and distributes
it uniformly over the rotor area; and (b) the actuator-disk model with rotation (ADM-R), which uses the blade-element theory
to calculate the lift and drag forces (that produce both thrust and rotation), and distribute them over the rotor disk based
on the local blade and flow characteristics. Simulation results are compared to high-resolution measurements collected with
hot-wire anemometry in the wake of a miniature wind turbine at the St. Anthony Falls Laboratory atmospheric boundary-layer
wind tunnel. In general, the characteristics of the wakes simulated with the proposed LES framework are in good agreement
with the measurements in the far-wake region. The ADM-R yields improved predictions compared with the ADM-NR in the near-wake
region, where including turbine-induced flow rotation and accounting for the non-uniformity of the turbine-induced forces
appear to be important. Our results also show that the Lagrangian scale-dependent dynamic SGS model is able to account, without
any tuning, for the effects of local shear and flow anisotropy on the distribution of the SGS model coefficient.
The `local scaling' hypothesis, first introduced by Nieuwstadt two decades ago, describes the turbulence structure of stable boundary layers in a very succinct way and is an integral part of numerous local closure-based numerical weather prediction models. However, the validity of this hypothesis under very stable conditions is a subject of on-going debate. In this work, we attempt to address this controversial issue by performing extensive analyses of turbulence data from several field campaigns, wind-tunnel experiments and large-eddy simulations. Wide range of stabilities, diverse field conditions and a comprehensive set of turbulence statistics make this study distinct.
With the aid of an extensive data set, collected at the Alsvik wind farm in Sweden, analytical expressions for the relative velocity deficit and the added turbulence of the flow generated by the wind turbines are derived. The general hypothesis is that the flow is determined by two factors: the efficiency of the turbine and the time that has elapsed since the air passed the rotor. It is found that the lateral and vertical distribution of the relative velocity deficit can be described as Gaussian. The constants in the equation are turbine and site dependent. The expansion rate of the wake is proportional to the square root of the transport time, which is also the case for laboratory free shear flows. An empirical expression is constructed for the added turbulence intensity in the wake. The dominating turbulence in the wake is generated by the strong wind shear. A method for calculating turbulence spectra in the wake is also presented. The results are validated against measurements from Alsvik as well as from two other sites.
The wake of a single wind turbine was modelled and the results used in modelling the power losses in a wind farm due to wake interference. Relevant parameters are reviewed, and simple algebraic expressions are developed to described the evolution of the wake. Model results are compared with measurements from a wind farm in northeast Spain.
The instability of the hub vortex observed in wind turbine wakes has recently been studied by Iungo et al. (J. Fluid Mech., vol. 737, 2013, pp. 499-526) via local stability analysis of the mean velocity field measured through wind tunnel experiments. This analysis was carried out by neglecting the effect of turbulent fluctuations on the development of the coherent perturbations. In the present paper, we perform a stability analysis taking into account the Reynolds stresses modelled by eddy-viscosity models, which are calibrated on the wind tunnel data. This new formulation for the stability analysis leads to the identification of one clear dominant mode associated with the hub vortex instability, which is the one with the largest overall downstream amplification. Moreover, this analysis also predicts accurately the frequency of the hub vortex instability observed experimentally. The proposed formulation is of general interest for the stability analysis of swirling turbulent flows.
An experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m(2) s(-3)) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).
This paper presents a technique to measure the time series of the three components of the wind vector at a point in space from synchronous measurements of three scanning Doppler wind lidars. Knowing the position of each lidar on the ground and the orientation of each laser beam allows for reconstructing the three components of the wind velocity vector. The laser beams must intersect at the desired point in space and their directions must be noncoplanar, so that trigonometric relationships allow the reconstruction of the velocity vector in any coordinate system.
This technique has been tested during a measurement campaign carried out at Cabauw’s Experimental Site for Atmospheric Research (CESAR) in the Netherlands and compared against measurements from sonic anemometers installed in a meteorological mast. The spatial resolutions of both measurement techniques differ by one order of magnitude. Therefore, in order to properly compare the results, a pseudospatial filter that mimics the attenuation induced by the lidar technology at small scales of turbulence has been applied to the velocity time series provided by the sonic anemometer.
Good agreement between both measurement systems is found in terms of the measured instantaneous velocity vector, turbulence statistics, Reynolds stresses, and the spectra of the three components of the velocity and the turbulent kinetic energy. These results provide a successful validation of the proposed technique.
Wind tunnel measurements were performed for the wake produced by a three-bladed wind turbine immersed in uniform flow. These tests show the presence of a vorticity structure in the near-wake region mainly oriented along the streamwise direction, which is denoted as the hub vortex. The hub vortex is characterized by oscillations with frequencies lower than that connected to the rotational velocity of the rotor, which previous works have ascribed to wake meandering. This phenomenon consists of transversal oscillations of the wind turbine wake, which might be excited by the vortex shedding from the rotor disc acting as a bluff body. In this work, temporal and spatial linear stability analyses of a wind turbine wake are performed on a base flow obtained with time-averaged wind tunnel velocity measurements. This study shows that the low-frequency spectral component detected experimentally matches the most amplified frequency of the counter-winding single-helix mode downstream of the wind turbine. Then, simultaneous hot-wire measurements confirm the presence of a helicoidal unstable mode of the hub vortex with a streamwise wavenumber roughly equal to that predicted from the linear stability analysis.
The intention of this study is to propose and validate a simple and
efficient analytical model for the prediction of the wake velocity
downwind of a stand-alone wind-turbine. Extensive efforts have been
carried out to model the wake region analytically. One of the most
popular models, proposed by Jensen, assumes a top-hat distribution of
the velocity deficit at any plane perpendicular to the wake. That model
has been extensively used in the literature and commercial softwares,
but it has two important limitations that should be pointed out: (a)
Even though this model is supposed to satisfy momentum conservation, in
reality mass conservation is only used to derive it; (b) the assumption
of a top-hat distribution of the velocity deficit is expected to
underestimate that deficit in the center of the wake, and overestimate
it near the edge of the wake. In order to overcome the above-mentioned
limitations, here we propose an alternative analytical model that
satisfies both mass and momentum conservation, and assumes a Gaussian
distribution of the velocity deficit. For this purpose, we apply
momentum and mass conservation to two different control volumes which
have been previously used in the context of analytical modeling of
wakes. The velocity profiles obtained with our proposed model are in
good agreement with large-eddy simulation data and experimental
measurements. By contrast, the top hat models, as expected, clearly
underestimate the velocity deficit at the center of the wake region and
overestimate it near the edge of the wake.
A detailed measurement program was undertaken in the summer of 1982 to characterize the wake behind the MOD-2, 2.5 MW, 91 m dia. wind turbine generators (WTGs) at Goodnoe Hills, Washington. Wind measurements were taken at 2 rotor diameters (2D) upwind and at 3, 5, 7 and 9D downwind WTGs 1 and 3 under operating and non-operating conditions. The measurement methodology using kite anemometry was developed to measure the wake. Most of the wake measurements were made under stable night-time flow, and the results indicated that the wake velocity deficits 9D downwind were on the order of 15–18 per cent, though, under more turbulent night-time flow, these deficits decreased to less than 10 per cent. These measured deficits were similar to those calculated from two different wake models.
The proper orthogonal decomposition technique is applied to 74 snapshots of 3D wind and temperature fields to study turbulent coherent structures and their interplay in the urban boundary layer over Oklahoma City, Oklahoma. These snapshots of data are extracted from single-lidar data via a four-dimensional variational data assimilation technique. The total velocities and fluctuating temperature are used to construct the data matrix for the decomposition; thus the first eigenmode represents the temporal mean of these data. Roll vortices with a wavelength-height ratio of 3.2 are identified in the first, most energetic eigenmode and are attributed to the inflection-point instability. The second and third spatial eigenmodes also exhibit roll characteristics with different time and length scales, resulting in clockwise-and counterclockwise-rotating roll vortices above the airport and the central business districts. Their positive correlation with temperature fluctuation suggests that those roll structures are driven by thermal as well as wind shear. Their limited horizontal extent seems to coincide with the path of the Oklahoma River. With decreasing rank, coherent structures undergo a transition from roll to polygon patterns. A localized downdraft or updraft located above a cluster of restaurants is captured by the fourth eigenmode. In the capping inversion layer, gravity wave eigenmodes are observed and may be attributed to convection waves. The representation of instantaneous snapshots by high-ranking eigenmodes is then examined by reconstruction of reduced-order fields. It is found that the first four eigenmodes are sufficient to capture the overall characteristics of the 74 snapshots of data.