Wind turbine wakes for wind energy

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

The effect of the boundary layer on the wake of a horizontal axis wind turbine

A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow

<strong class="journal-contentHeaderColor">Abstract.</strong> The development of wind energy in recent years has made the representation of wind turbines in numerical models essential for wind resource and environmental assessment. In this study, we introduce a new wind turbine model, the Simple Actuator Disk for Large Eddy Simulation (SADLES), parameterized in the Weather Research and Forecast (WRF) model. Our goal is to perform realistic downscaling of large eddy simulation, so we integrated SADLES and the cell perturbation method as WRF modules and released the code as open-source for further research. We demonstrate the effectiveness of SADLES by comparing a large eddy simulation of a 5-MW wind turbine using the WRF-SADLES with the Parallelized Large-eddy simulation Model (PALM), showing that SADLES can simulate wind turbine wakes effectively at an intermediate resolution of a few dozen meters. Furthermore, we show the applicability of SADLES in wind farm assessment by downscaling the ERA5 reanalysis data to study turbine-to-turbine and farm-to-farm interactions for the Alpha Ventus wind farm. In our case study, due to the wind farm to the southwest, the ambient wind speed at Alpha Ventus is reduced by 14 % and the average turbine power is reduced by 35 %. SADLES provides a promising balance between computational efficiency and accuracy for wind turbine wake simulations and has potential applications in wind energy assessment and wind farm construction planning.

The wake of a horizontal-axis wind turbine was studied at a Reynolds number of $Re_D=4\times 10^6$ with the aim of revealing the effects of the tip speed ratio, $\lambda$ , on the wake. Tip speed ratios of $4\lt \lambda \lt 7$ were investigated and measurements were acquired up to 6.5 diameters downstream of the turbine. Through an investigation of the turbulent statistics, it is shown that the wake recovery was accelerated due to the higher turbulence levels associated with lower tip speed ratios. The energy spectra indicate that larger broadband turbulence levels at lower tip speed ratios contributes to a more rapidly recovering wake. Wake meandering and a coherent core structure were also studied, and it is shown that these flow features are tip speed ratio invariant, when considering their Strouhal numbers. This finding contradicts some previous studies regarding the core structure, indicating that the structure was formed by a bulk rotor geometric feature, rather than by the rotating blades. Finally, the core structure was shown to persist farther into the near wake with decreasing tip speed ratio. The structure’s lifetime is hypothesised to be related to its strength relative to the turbulence in the core, which decreases with increasing tip speed ratio.

With the increasing integration of the wind power into the power system, wind farm are required to be controlled as a single unit and have all the same control tasks as conventional power plants. The wind farm central controller receives control orders from Transmission System Operator (TSO), then dispatch the wind power reference to each wind turbine. One of the most commonly used dispatch methods is to dispatch the wind power reference to each wind turbine proportional to each wind turbine's available wind power without the consideration of the wake effect. The wake which depends on the thrust efficient of upstream wind turbines in the wind farm influences the downstream wind speed which determines the available wind power of the downstream wind turbine. Optimize the wind power production of each wind turbine in the wind farm by the optimization of the pitch angle and tip-speed-ratio of each turbine can increase the total available wind power of the wind farm. This paper proposed a new dispatch method which dispatches the wind power reference to each wind turbine proportional to each wind turbine's optimal wind power to increase the available wind power of the whole wind farm. Particle Swarm Optimization (PSO) is used to obtain the optimal wind power for each wind turbine. A case study is carried out. The available wind power of the wind farm was compared between the traditional dispatch method and the proposed dispatch method with the consideration of the wake effect.

The wake behind a wind turbine in an atmospheric boundary layer (ABL) is investigated using a numerical simulation method. From the viewpoint of cost effectiveness, collective installations (wind farms) are desired because they can reduce the total length of the power-transmission lines and labor costs for maintenance. However, a wake from an upwind turbine may significantly reduce power production downstream and cause large load variations on the blades. Numerical methods based on computational fluid dynamics (CFD) are efficient for investigating the structures and characteristics of wind-turbine wakes. This study mainly discusses the effect of wind shear of ABL on a wind turbine wake by focusing on the behavior of tip vortices and recovery process of velocity deficits. It is found that the wind shear mainly influence on near-wake structure and the effect on far-wake is relatively weak.

Increasing use of wind energy over the years results in more and larger clustered wind farms. It is therefore fundamental to have an in-depth knowledge of wind-turbine wakes, and especially a better understanding of the well-known but less understood wake-meandering phenomenon which causes the wake to move as a whole in both horizontal and vertical directions as it is convected downstream. This oscillatory motion of the wake is crucial for loading on downstream turbines because it increases fatigue loads and in particular yaw loads. In order to address this phenomenon, experimental investigations were carried out in an atmospheric-boundary-layer wind tunnel using a 3 × 3 scaled wind farm composed of three-bladed rotating wind-turbine models subject to a neutral atmospheric boundary layer (ABL) corresponding to a slightly rough terrain, i.e. to offshore conditions. Particle Image Velocimetry (PIV) measurements were performed in a horizontal plane, at hub height, in the wake of the three wind turbines in the wind-farm centreline. From the PIV velocity fields obtained, the wake-centrelines were determined and a spectral analysis was performed to obtain the characteristics of the wake-meandering phenomenon. In addition, Hot-Wire Anemometry (HWA) measurements were performed in the wakes of the same wind turbines to validate the PIV results. The spectral analysis performed with the spatial and temporal signals obtained from PIV and HWA measurements respectively, led to Strouhal numbers St = fD/Uhub ≃ 0.20 − 0.22.

The expansion of wind energy development has resulted in larger wind farms and closer placement of turbines to utilize the space available. Each turbine produces a wake that affects downstream turbines, which causes issues such as production loss, blade loading, and fluctuating electrical output. To minimize this impact, the wake produced by turbines must first be understood. Experimental methods were used in the exploration of the wake effects of wind turbines. Smoke visualization inside the University of Waterloo Wind Generation Facility was used to determine the helical vortex wake distribution behind a 3.3 m diameter turbine as well as the tip vortex shedding from the blade. Wind Doppler Light Detection and Ranging (LiDAR) measurement devices were modified and used to measure wind turbine wake velocities. The University of Waterloo Wind Energy Group has a ZephIR z150 LiDAR to use in this study. The LiDAR was verified under normal operation for accuracy against a cup and vane anemometer. The LiDAR was then verified for accuracy and to determine the position of measurements after modifications necessary for future wake measurement experiments.

Abstract. Terrain-induced flow phenomena modulate wind turbine performance and wake behavior in ways that are not adequately accounted for in typical wind turbine wake and wind plant design models. In this work, we simulate flow over two parallel ridges with a wind turbine on one of the ridges, focusing on conditions observed during the Perdigão field campaign in 2017. Two case studies are selected to be representative of typical flow conditions at the site, including the effects of atmospheric stability: a stable case where a mountain wave occurs (as in ∼ 50 % of the nights observed) and a convective case where a recirculation zone forms in the lee of the ridge with the turbine (as occurred over 50 % of the time with upstream winds normal to the ridgeline). We use the Weather Research and Forecasting Model (WRF), dynamically downscaled from the mesoscale (6.75 km resolution) to microscale large-eddy simulation (LES) at 10 m resolution, where a generalized actuator disk (GAD) wind turbine parameterization is used to simulate turbine wakes. We compare the WRF–LES–GAD model results to data from meteorological towers, lidars, and a tethered lifting system, showing good qualitative and quantitative agreement for both case studies. Significantly, the wind turbine wake shows different amounts of vertical deflection from the terrain and persistence downstream in the two stability regimes. In the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub height at four rotor diameters downstream. In the convective case, the wake deflects above the recirculation zone over 40 m above hub height at the same downstream distance. Overall, the WRF–LES–GAD model is able to capture the observed behavior of the wind turbine wakes, demonstrating the model's ability to represent wakes over complex terrain for two distinct and representative atmospheric stability classes, and, potentially, to improve wind turbine siting and operation in hilly landscapes.

Through wind farm control, the negative impact of the wake effect on power production could be mitigated. In previous research on wind farm control, the centralized optimization method was mostly employed due to its simplicity. However, centralized optimization control methods will face difficulties for large wind farms, in which more complex wake coupling relationships among wind turbines will make the optimization highly inefficient. In order to boost the efficiency of the optimization, the weighted graph wake decoupling (WGWD) method is proposed in the current research, which decomposes the complex optimization problem into accessible sub-problems solved in parallel. A wake weighted graph portraying the wake coupling relationship among wind turbines has been established. The decoupling approach applied on a weighted graph partitions the wind farm into uncoupled subsets of wind turbines, simplifying the optimization problem. By using distributed optimization method, the yaw angles of wind turbines in each subset have been optimized to maximize the power generation of the subset. The results show that the proposed method has high computational efficiency and can also improve wind farm total power production. Taking advantage of its high efficiency, the proposed wind farm optimization control method is suitable for future real-time active yaw control of large wind farms.

As wind-turbine arrays continue to be installed and the array size continues to grow, there is an increasing need to represent very large wind-turbine arrays in numerical weather prediction models, for wind-farm optimization, and for environmental assessment. We propose a simple analytical model for boundary-layer flow in fully-developed wind-turbine arrays, based on the concept of sparsely-obstructed shear flows. In describing the vertical distribution of the mean wind speed and shear stress within wind farms, our model estimates the mean kinetic energy harvested from the atmospheric boundary layer, and determines the partitioning between the wind power captured by the wind turbines and that absorbed by the underlying land or water. A length scale based on the turbine geometry, spacing, and performance characteristics, is able to estimate the asymptotic limit for the fully-developed flow through wind-turbine arrays, and thereby determine if the wind-farm flow is fully developed for very large turbine arrays. Our model is validated using data collected in controlled wind-tunnel experiments, and its usefulness for the prediction of wind-farm performance and optimization of turbine-array spacing are described. Our model may also be useful for assessing the extent to which the extraction of wind power affects the land–atmosphere coupling or air–water exchange of momentum, with implications for the transport of heat, moisture, trace gases such as carbon dioxide, methane, and nitrous oxide, and ecologically important oxygen.

Similar to other renewable energy sources, wind energy is characterized by a low power density. Hence, for wind energy to make considerable contributions to the world's overall energy supply, large wind farms (on- and offshore) consisting of arrays of ever larger wind turbines are being envisioned and built. From a fluid mechanics perspective, wind farms encompass turbulent flow phenomena occurring at many spatial and temporal scales. Of particular interest to understanding mean power extraction and fluctuations in wind farms are the scales ranging from 1 to 10 m that comprise the wakes behind individual wind turbines, to motions reaching 100 m to kilometers in scale, inherently associated with the atmospheric boundary layer. In this review, we summarize current understanding of these flow phenomena (particularly mean and second-order statistics) through field studies, wind tunnel experiments, large-eddy simulations, and analytical modeling, emphasizing the most relevant features for wind farm design and operation.

This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons, and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. Additionally, the correlation between the smoothing effect of aggregated wind farms and the forecast accuracy for the corresponding aggregated power output is examined. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling one-step-ahead wind power forecasting with higher accuracy.