Yawed Wind Turbine Research Articles

Similarities in the meandering of yawed rotor wakes

This study investigated the meandering of yawed wind turbine rotor wakes, focusing on the similarities across different yaw angle scenarios. Spectrum analysis of velocity fluctuations reveals that the meandering of the yawed rotor wake is symmetrical about the wake center, despite its skewness. The non-zero lateral force of the yawed rotor enhances meandering in the lateral direction compared to the vertical direction. However, the lateral profiles of meandering strength exhibit similarities across different yaw angle scenarios, indicating a consistent wake meandering mode. The wake meandering frequency increases with the yaw angle. A relationship involving wake meandering frequency, drag coefficient, and yaw angle is formulated for wind turbine rotor wakes under different yaw angles. This relationship is also applicable to thin plate wakes within a certain range of inclination angles/yaw angles. The present study reveals the similarity in wake meandering characteristics across different yaw angle scenarios, which is instrumental in improving our understanding of wake meandering and in developing analytical wake models for wind turbines.

Dynamic individual pitch control for wake mitigation: Why does the helix handedness in the wake matter?

Wind farm flow control aims at mitigating wake effects in order to maximize power production in wind farms. This work mostly focuses on the Helix strategy, which relies on individual pitch control to radially offset the application point of the thrust force from the rotor center and to dynamically change its azimuthal position. Previous studies have shown that power gains for a downstream turbine are higher for a counter-clockwise (CCW) rotation of the application point than for a clockwise (CW) one. In the CCW case, the wake develops as a right-handed helix, while in the CW case, a left-handed helix is observed. Using Large Eddy Simulations, this paper shows that the helix handedness in the wake matters due to its interaction with the wake swirl. Results of the CCW and CW helix first highlight the formation of streamwise vorticity in the near wake, which is transformed into strong coherent vortices in the far wake. Those vortex structures, to some extent similar to the counter-rotating vortex pair in the wake of yawed wind turbines, are responsible for (i) displacing the wake thanks to their induced velocities and (ii) deforming the shape of the wake.

A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions

Wind turbine wakes significantly affect power production and impose higher loads on downstream turbines. Therefore, the development of accurate and efficient wake models is important for optimizing wind farm layouts and predicting wind turbine performance. This study introduces a novel analytical wake model for yawed wind turbines that incorporates the effects of the Coriolis force. The wake deflection in the far wake region is derived through the application of the principles of mass and momentum conservation. In the near wake, the deflection is assumed to be linear with distance. A Gaussian distribution is assumed for the velocity deficit within the wind turbine wake. Two approaches have been proposed to estimate the onset of the far wake region. While the first approach employs a simplified empirical formula, the second approach utilizes an iteration-based method. The proposed analytical wake model has been validated against computational fluid dynamics (CFD) results. Subsequently, the effects of several important parameters on the wake deflection have been systematically investigated. Overall, the simulation results showed a satisfactory agreement between the CFD results and those obtained from the proposed model. Furthermore, the study concluded that the Coriolis force can exert significant effects on wake deflection, particularly in the far wake region, confirming previous findings from numerical simulations. Due to its simplicity and computational efficiency, the proposed model can be readily used in several applications, including wind farm layout optimization, control and risk assessment.

Wake measurement of wind turbine under yawed conditions using UAV anemometry system

This study employs a UAV anemometry system to assess the wind field around a yawed wind turbine, particularly focusing on its wake during operational conditions. The research findings reveal that the evolution of wind turbine wakes follows distinct patterns at various downstream distances. Turbulence intensity notably amplifies within regions characterized by significant fluctuations in mean wind speed. Specifically, in yawed conditions, the areas with the highest turbulence generated by the rotor coincide with zones exhibiting pronounced variations in mean wind speed. The heightened turbulence within the wake region to some extent constrains the safety and economic viability of wind farms. Turbulence intensity increases significantly in the region where the average wind speed changes greatly, that is, under the yaw state of the wind turbine, this region is characterized with the strongest turbulence generated by the rotor. The UAV anemometry system's wind speed assessment closely matches predictions from the Y-3DJGF model, accounting for wake experience coefficient adjustments. Furthermore, in yaw conditions, the wind turbine's wake trajectory exhibits some deviation from the incident flow's direction, with an initial increase in slope followed by gradual stabilization. As the downstream distance increases, the trajectory will eventually establish a consistent trend with the incident flow. The cost-effective and flexible UAV anemometry system enhances wind field measurements, offering an innovative approach for wind energy sector research and engineering.

Influence of wind gusts on aeroelastic loads of NREL 5MW wind turbine

This study employs blade-resolved computational fluid dynamics simulations to investigate the aero-elastic loads on a yawed NREL 5MW wind turbine subject to full and partial wind gusts. A novel spatial gust representation, tailored towards application to large eddy simulations, is introduced. This methodology permits the injection of gusts with customizable spatial dimensions inside the flow domain. Simulations are conducted with gusts having the same diameter as the turbine rotor, half rotor diameter, and asymmetric impact with respect to the center of rotation of the turbine. Simulations of the turbine under yawed conditions are also considered. The flow characteristics are reported using contours and iso-surfaces of velocity magnitude and Q-criteria. Insights into the load characteristics of the turbine are described using torque, thrust, and root bending moments. This research highlights that partial gust impact contributes to noteworthy load fluctuations and blade tip deformations, stemming from the imbalance in force distribution across the rotor plane.

Stochastic Dynamical Modeling of Wind Farm Turbulence

Low-fidelity engineering wake models are often combined with linear superposition laws to predict wake velocities across wind farms under steady atmospheric conditions. While convenient for wind farm planning and long-term performance evaluation, such models are unable to capture the time-varying nature of the waked velocity field, as they are agnostic to the complex aerodynamic interactions among wind turbines and the effects of atmospheric boundary layer turbulence. To account for such effects while remaining amenable to conventional system-theoretic tools for flow estimation and control, we propose a new class of data-enhanced physics-based models for the dynamics of wind farm flow fluctuations. Our approach relies on the predictive capability of the stochastically forced linearized Navier–Stokes equations around static base flow profiles provided by conventional engineering wake models. We identify the stochastic forcing into the linearized dynamics via convex optimization to ensure statistical consistency with higher-fidelity models or experimental measurements while preserving model parsimony. We demonstrate the utility of our approach in completing the statistical signature of wake turbulence in accordance with large-eddy simulations of turbulent flow over a cascade of yawed wind turbines. Our numerical experiments provide insight into the significance of spatially distributed field measurements in recovering the statistical signature of wind farm turbulence and training stochastic linear models for short-term wind forecasting.

Wake asymmetry of yaw state wind turbines induced by interference with wind towers

Wind turbine wakes are known to cause significant reductions in power generation and increased load on downwind wind turbines. Therefore, controlling wind turbine wakes is crucial, and yaw steering is considered one of the most effective methods of doing so. In this study, wind tunnel experiments using a wind turbine model and Large-eddy simulations (LES) were conducted to gain a better understanding of the wake of a yawed wind turbine. The results showed a clear difference in the horizontal distribution of wake velocity at hub height for positive and negative yaw angles, as well as both lateral and vertical wake deflections. Further investigation revealed that wind towers have a significant effect on the wakes of yawing wind turbines. For positive yaw angles, the velocity deficit is larger above the rotor, resulting in a vertical wake shape caused by the interference between the blade wakes and detached flow from the tower. Conversely, for negative yaw angles, the velocity deficit is larger below the rotor, resulting in a horizontal wake shape. These findings will facilitate the development of more accurate wake models and control methods.

Study on Complex Wake Characteristics of Yawed Wind Turbine Using Actuator Line Method

In modern large-scale wind farms, power loss caused by the wake effect is more than 30%, and active yaw control can greatly reduce the influence of the wake effect by deflecting the wind turbine’s wake. The yawed wind turbine’s wake characteristics are complex, and a deep comprehension of a yawed turbine’s wake is necessary. The actuator line method combined with URANS (unsteady Reynold-averaged Navier–Stokes equations) is used to study the yawed wind turbine’s wake characteristics in this paper. Compared with an un-yawed wind turbine, a yawed one has two main characteristics, deflection and deformation. With an increasing yaw angle, turbine wake shows an increasing deflection. The results indicated that deflection at different height was different, the wake profile showed the biggest deflection at about the hub height, while the smallest deflection existed at the top and bottom of the yawed turbine’s wake. This can be visually demonstrated by the evolution of a kidney-shape velocity distribution at the vertical cross-section. Two-dimensional and three-dimensional presentations of velocity deficit distributions are presented in this paper. The evolution of an irregular kidney-shape distribution is discussed in this paper. It is formed by the momentum exchange caused by the counter-rotating vortex pair. The results indicated that the counter-rotating vortex pair was composed of the streamwise vortex flux brought by the tip vortex. Furthermore, when the wind turbine rotated clockwise and yawed clockwise, the negative vorticity of counter-rotating vortex first appeared in the upper left position.

Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification

WTs are in yaw during most of the operating time. The wake effect of the yawed Wind turbine (WT) leads to lower wind speed and increased turbulence in the wake region of the WT, which leads to increased dynamic load and lower output power of the downstream WT and limits the safety and economic efficiency of the wind farm to a certain extent. Inspired by the 3DJGF model and Jiménez model, this paper proposes a Yawed-3D Jensen-Gaussian full wake (Y-3DJGF) model considering wind shear and double Gaussian distribution over the wake region which is computationally inexpensive and efficient with the 3D wake distribution characteristics of the yaw WT can be obtained. Moreover, a Doppler Light Detection and Ranging (LiDAR)-based field experimental is conducted in a wind farm in North China, and the SCADA data are integrated to analyze the wake data under yaw conditions and verify the accuracy of the Y-3DJGF model with the verification of the proposed model. The results showed that the average of the relative error between the Y-3DJGF model and the measured data was 4.974%, and the fit with the measured data was better than other 5 typical models. Works in this paper can provide reference for improving the energy output of WT and dynamic load analysis of downstream WT.

Modelling the induction, thrust and power of a yaw-misaligned actuator disk

Collective wind farm flow control, where wind turbines are operated in an individually suboptimal strategy to benefit the aggregate farm, has demonstrated potential to reduce wake interactions and increase farm energy production. However, existing wake models used for flow control often estimate the thrust and power of yaw-misaligned turbines using simplified empirical expressions that require expensive calibration data and do not extrapolate accurately between turbine models. The thrust, wake velocity deficit, wake deflection and power of a yawed wind turbine depend on its induced velocity. Here, we extend classical one-dimensional momentum theory to model the induction of a yaw-misaligned actuator disk. Analytical expressions for the induction, thrust, initial wake velocities and power are developed as a function of the yaw angle ( $\gamma$ ) and thrust coefficient. The analytical model is validated against large eddy simulations of a yawed actuator disk. Because the induction depends on the yaw and thrust coefficient, the power generated by a yawed actuator disk will always be greater than a $\cos ^3(\gamma )$ model suggests. The power lost due to yaw misalignment depends on the thrust coefficient. An analytical expression for the thrust coefficient that maximizes power, depending on the yaw, is developed and validated. Finally, using the developed induction model as an initial condition for a turbulent far-wake model, we demonstrate how combining wake steering and thrust (induction) control can increase array power, compared to either independent steering or induction control, due to the joint dependence of the induction on the thrust coefficient and yaw angle.

Evolution mechanism of wind turbine wake structure in yawed condition by actuator line method and theoretical analysis

Through deflecting the wind turbine wake and reducing the influence of wake effect, wind farm cooperative control can enhance the total power generation of wind farm. In addition to wake deflection, the wake of yawed wind turbine shows complex distortion characteristics, which cannot be explained sufficiently by traditional momentum conservation. In this paper, numerical simulation using Actuator Line Method and theoretical analysis were adopted to investigate the wake characteristics of NREL 5 MW wind turbine in yaw. The wake of yawed wind turbine shows a velocity deficit distribution with kidney-shape. Due to the periodic change of tip vortex shedding velocity, the vorticity carried by tip vortex changes periodically, leading to the formation of an asymmetric counter-rotating vortex pair (CVP), which induced the complex wake structure of yawed wind turbine. On this basis, a new theoretical model for the wake structure of yawed wind turbine is proposed, which can accurately predict the main characteristics of the CVP. This paper reveals the wake evolution mechanism of yawed wind turbine and establishes the corresponding theoretical model, which is conducive to the development of high-precision engineering wake model and the optimization of wind farm cooperative control.

A wind tunnel investigation of yawed wind turbine wake impacts on downwind wind turbine performances and wind loads

In the current work we experimentally explored yawed wind turbine wake impacts on downwind wind turbine performances and wind loads. The lab-scale wind turbine model with a rotor diameter ( D) of 0.442 m and a height of 1 m (=2.26 D) was installed in a closed-circuit boundary layer wind tunnel (test section: 15 m long × 3.6 m wide × 2.0 m high) of the Research Institute for Applied Mechanics (RIAM) of Kyushu University. Power performance tests were initially conducted with a single rotor in isolation in order to characterize a rotor’s power output in stand-alone conditions. A detailed comparison of the tests revealed that the power output decreased rapidly as the yaw angle (γ) increased. It is presumed that the power output decrease in yawed cases is mainly due to the decrease in the effective rotor area and the change in the angle of the incoming wind flow with respect to the wind turbine blade. Next, using two wind turbine models aligned with the dominant inflow direction, the merging wakes behaviors caused by three different lateral separation distances were tested: (a) Case 1 ( y = 0), (b) Case 2 ( y = 0.5 D), and (c) Case 3 ( y = 1 D). Here, the separation distance between the two wind turbine models was fixed at 6 D in all cases. Extremely large power output deficits of 46%–76% were seen in the Case 1 configuration. This is mainly due to the significant wake velocity deficits induced by the upwind wind turbine model. In the Case 2 configuration with γ values of 20° and 30°, a significant increase in the power output of the downwind wind turbines was observed. Similar to Case 1 configuration, these results are considered to be mainly due to the upwind turbine-induced wake velocity deficits and wake deflection. Finally, in the Case 3 configuration, no significant difference was found in all of the results, and the tendency was almost the same. We show that the wake velocity deficits induced by the upwind wind turbine model had almost no effect on the power output of the downwind wind turbine model. We evaluated the total power output of the two turbines. As a result, in the Case 2 configuration with 20° yaw angle, the total power output of the two wind turbine models was the highest due to the increase in the power output of the downwind wind turbine model. In order to investigate the main cause of the significant increase in the power output of the downwind wind turbine model at 20° and 30° yaw angles in the Case 2 configuration, we measured the lateral wind speed distribution at the 6 D position on the downwind side of the upwind wind turbine model by using the ultrasonic anemometer. As a results, it was clarified that the peak of the wake velocity deficits induced by the upwind wind turbine model is clearly shifted away from the downwind turbine such that it experiences a smaller deficit due to wake steering. Also, with wake steering the upwind turbine-induced wake velocity deficits may be smaller due to the reduction in rotor area. Finally, it is extremely important to understand the wind load acting on the downwind wind turbine model operating within the wake region induced by the upwind yawed wind turbine model when the maximum power output is generated. It can be seen that as the yaw angle of the upwind wind turbine model increased, the power output generated by the downwind wind turbine model and the streamwise wind load acting on it also increased. However, it was also clarified that the streamwise wind load acting on the downwind wind turbine model in this situation did not exceed the stand-alone value.

Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow

This work presents a new engineering analytical model that predicts the effect of both the turbine yaw misalignment and the inflow wind veer on the wake flow distribution downwind of a wind turbine. To consider the veered inflow, two methods were examined. In the first method, the curled shape of the wake due to the yaw offset is initially modelled. The wake shape is then laterally skewed at each height due to the wind veer based on the assumption that the turbine wake is transported downstream by the incoming flow. The second method is a more realistic approach that accounts for the effect of wind veer on the wind velocity direction and the yaw angle seen by the wind turbine. This models the wake region in a local coordinate system defined based on the wind direction at each height. A coordinate transformation is then performed to represent the wake flow distribution in the global coordinate system attached to the ground. The results show that while the two methods provide similar outputs for small variations in the wind direction across the rotor, the difference becomes more evident with an increase in wind veer. High-fidelity simulations for a turbine subject to a neutral atmospheric boundary layer were employed to validate model predictions for different operating conditions.

Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow

Large Eddy Simulation (LES) is used to study the wind veer effects on the wake of a yawed wind turbine. The veer deflects the wake in the spanwise direction, and this effect can be captured in the wake model by adding a veer correction term. The counter-rotating vortex pair structures previously observed behind yawed turbines can also be recovered by subtracting the background veer vorticity.

A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control

Yaw control is one of the most promising active wake control strategies to maximize the total power generation of wind farms. Meanwhile, structural performance needs to be considered in yaw misalignment in case the adverse structural performance offsets the benefit of yaw control in power enhancement. However, an efficient and accurate prediction method for fatigue loads under yaw control is still lacking. In this study, a machine learning-based prediction method is proposed to accurately estimate the fatigue loads and power of wind turbines under yaw control. Fatigue loads at critical turbine components and corresponding power yields are selected as outputs to reflect the performance of yawed wind turbines. Since most wind turbines (WTs) are sunk into the wake flow of their upstream counterparts, the wake effects are considered with the combination of active yaw control. Besides, the full range of inflow and yaw conditions are taken into account to ensure the accuracy and practicability of the proposed model. A machine learning algorithm, support vector regression (SVR), is tuned and trained to learn the relationships between outputs and inputs. The superiority of the proposed method is verified by comparing it with another machine learning-based model in several metrics. The results show that the proposed prediction method can return high regression coefficients and low deviation, proving its accuracy and robustness. Large yaw angles and high wind speeds are found to be beneficial for further improving the prediction accuracy. The proposed fatigue loads and power prediction method is expected to make contributions to the yaw optimization and therefore benefit the wind farms.

Wake Structure in Yawed Approaching Flows for an Axial-Flow Wind Turbine

Abstract Effects of yawed wind turbines on the wake structure are not fully understood. To obtain a better understanding, numerical studies of a small-scale three-bladed horizontal axis wind turbine at tip speed ratio (TSR) = 6.7 with yaw angles of zero, 15 deg, 30 deg, and 45 deg have been carried out to investigate the wake characteristics of the turbine in the near- and farwake. A hybrid approach coupling large eddy simulation (LES) with actuator line modeling (ALM) has been employed in the present study. The predicted results confirm the previous finding that the turbine wake is asymmetric under yawed approaching flows and the wake is inclined to the direction where the rotor is yawed. The present work further demonstrates that at high yaw angles the main wake may be divided into two smaller parallel wakes further downstream that are not symmetric suggesting a dependency on the turbine rotation direction. This study quantitatively explains how the nonuniform variations of radial velocity components at the turbine plane caused by the yawed flows result in the wake deflection and intersection of hub and blade tip vortices further downstream which leads to the wake splitting at high yaw angles.

Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering

The yaw control of horizontal-axis wind turbines is the key point for wake redirection, which is expected to provide an improvement in the balance between wind farm production and loads. For multi-MW upwind wind turbines, there are substantially two possibilities for obtaining a desired demanded yaw angle: using yaw actuators or Individual Pitch Control (IPC). A detailed balance of the pros and cons of both approaches requires in-depth studies. On these grounds, this work is a comprehensive experimental characterization of the yaw by individual pitch control (yaw-by-IPC). The experiments are conducted at the R. Balli wind tunnel of the University of Perugia on a fully instrumented wind turbine scaled model, named G1, developed by the Technical University of Munich. The objective of the study is twofold: assessment of the performance of the yaw-by-IPC in terms of yaw tracking capabilities and impact on power, loads, and actuators usage; analysis of its impact on the shed wake. The wake has been characterized on a plane parallel to the rotor at a distance of 1.82 diameters by imposing several steady yaw references, while wind intensity measurements have been performed through a traversing system hosting a hot wire anemometer. Each target of the analysis has been characterized for the yaw-by-IPC and for the control based on yaw actuation, in order to perform a comparison. With laminar inflow, it results that the yaw-by-IPC follows very well the demanded reference, even with a slight improvement in the case of dynamic yaw reference, and no power losses are visible. Concerning the wake shed by a yawed G1 with yaw-by-IPC, a higher flow speed is observed immediately behind the windward side of the rotor than with geared yaw actuation. It results, therefore, that yaw-by-IPC applied to a yawed wind turbine may increase the available wind power at a downstream machine.

Extension and validation of an operational dynamic wake model to yawed configurations

This work extends the capabilities of an operational dynamic wake model to yawed cases. The proposed framework brings together flow sensing and Lagrangian flow modeling into a unified framework: both the freestream flow field and the wake one are discretized as series of information-carrying particles. A source condition for these particles is thus obtained from the wind turbine measurements through flow sensing techniques. The estimated flow field state across the wind farm is finally reconstructed by propagating these particles downstream at their own characteristic velocity. The resulting framework is first presented and its extension to yawed turbine is then discussed. Comparison against high-fidelity Large Eddy Simulations of yawed wind turbines confirms the good potential of the approach: different yaw angles are considered and the performance of the model are evaluated. This study indicates that the proposed framework captures the relevant large scale wake features caused by the combined effect of yawing and wake meandering at a low computational cost thereby making it suitable for online model-based control.

Research on Unsteady Wake Characteristics of the NREL 5MW Wind Turbine Under Yaw Conditions Based on a LBM-LES Method

The yaw of a large-scale wind turbine will change the aerodynamic performance of its substructures. In view of this, this article applied a LBM-LES method to numerically simulate the yawed MEXICO wind turbine and compared the numerical results with the experimental data collected in the New MEXICO experiment to verify the reliability of the method. A rapid dynamic yaw is defined with a yaw speed of 5°/s in yaw control, while a slow dynamic yaw is with a yaw speed of 0.3°/s. The NREL 5 MW offshore wind turbine was used to explore the constant wake characteristics under the conditions of a rapid dynamic yaw and of a slow dynamic yaw. It can be seen from the results that the LBM-LES method captures the detailed characteristics of the complex unsteady flow field of the wind turbine. In the rapid dynamic yaw process, the transition area between the far wake and the near wake shows a large deflection. However, in the process of the slow dynamic yaw, the far wake deflects forward relatively to the yaw. The dynamic yaw wake forms a kidney-shaped cross-section similar to the one in a steady yaw. The yaw wake recovers faster than the wake without yaw, and the rapid dynamic yaw wake has an asymmetric structure. The thrust of the wind turbine drops rapidly during the rapid dynamic yaw, while the thrust of the slow dynamic yaw decreases gradually. The thrust drops three times per rotor revolution, and the time of thrust decrease is delayed as the yaw angle increases.

Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow

Abstract. Redirecting the wake from an upstream wind turbine by yawing its rotor can reduce the negative impact of the wake on a downstream turbine. The present research investigated wind turbine wake behaviour for three yaw angles [-30,0,30∘] at different inflow turbulence levels and shear profiles under controlled conditions. Experiments were conducted using a model wind turbine with 0.6 m diameter (D) in a large wind tunnel. A short-range lidar WindScanner was used to map the wake with high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height. The lidar WindScanner enabled fast measurements at multiple locations in comparison to the standard hot-wire measurements. The flow structures and the energy dissipation rate of the wake were measured from 1 up to 10 D, and for one inflow case up to 16 D, downstream of the turbine rotor. A strong dependency of the wake characteristics on both the yaw angle and the inflow conditions was observed. In addition, the curled wake that develops under yaw misalignment due to the counter-rotating vortex pair was more pronounced with a boundary layer (sheared) inflow condition than for uniform inflow with different turbulence levels. Furthermore, the lidar velocity data and calculated quantities such as the energy dissipation rate compared favourably with hot-wire data from previous experiments with a similar inflow condition and wind turbine model in the same facility, lending credibility to the measurement technique and methodology used here. The results of this measurement campaign provided a deeper understanding of the development of the wake for different yaw angles and inflow conditions which can help improve wake models.