Wake Steering Research Articles

Assessment of yaw-control effects on wind turbine-wake interaction: A coupled unsteady vortex lattice method and curled wake model analysis

Downstream wind turbines in a wind farm are affected by the wake of the upstream turbine. Therefore, wake steering has been introduced to alleviate the wake effect by yawing the shaft of the upstream wind turbine. In this scenario, the upstream and downstream wind turbines encounter non-uniform inflow in wake steering. In this study, the unsteady vortex lattice method (UVLM) was coupled with the curled wake model to consider the unsteady aerodynamics resulting from the wake steering. The proposed method was validated with the National Renewable Energy Laboratory (NREL) Phase VI experiment and Large Eddy Simulation (LES) data simulated with two-turbine case. Three NREL 5 MW wind turbines were simulated in wake steering conditions, and the power performance of the individual turbines and their root flapwise bending moments were investigated. The flow field investigation indicated that the counter-rotating vortices significantly influence the effective angle of attack and aerodynamic loads. In addition, a secondary effect was observed on the effective angle of attack of the rotor and wake deflection. The simulation results demonstrated the applicability of the UVLM in wake steering and its potential for analyzing the wake effects experienced by multiple wind turbines in a wind farm.

A data-driven machine learning approach for yaw control applications of wind farms

This study proposes a cost-effective machine-learning based model for predicting velocity and turbulence kinetic energy fields in the wake of wind turbines for yaw control applications. The model consists of an auto-encoder convolutional neural network (ACNN) trained to extract the features of turbine wakes using instantaneous data from large-eddy simulation (LES). The proposed framework is demonstrated by applying it to the Sandia National Laboratory Scaled Wind Farm Technology facility consisting of three 225 kW turbines. LES of this site is performed for different wind speeds and yaw angles to generate datasets for training and validating the proposed ACNN. It is shown that the ACNN accurately predicts turbine wake characteristics for cases with turbine yaw angle and wind speed that were not part of the training process. Specifically, the ACNN is shown to reproduce the wake redirection of the upstream turbine and the secondary wake steering of the downstream turbine accurately. Compared to the brute-force LES, the ACNN developed herein is shown to reduce the overall computational cost required to obtain the steady state first and second-order statistics of the wind farm by about 85%.

Enabling control co-design of the next generation of wind power plants

Abstract. Layout design and wake steering through wind plant control are important and complex components in the design and operation of modern wind power plants. They are currently optimized separately, but with more and more computational and experimental studies demonstrating the gains possible through wake steering, there is a growing need from industry and regulating bodies to combine the layout and control optimization in a co-design process. However, combining these two optimization problems is currently infeasible due to the excessive number of design variables and large solution space. In this article, we present a method that enables the coupled optimization of wind power plant layout and wake steering with no additional computational expense than a traditional layout optimization. We developed a geometric relationship between wind turbines to find an approximate optimal yaw angle, bypassing the need for either a nested or coupled wind plant control optimization. It also provides a significant and immediate improvement to wind power plant design by enabling the co-design of turbine layout and yaw control for wake steering. A small co-designed plant shown in this article produces 0.8 % more energy than its sequentially designed counterpart. This additional energy production comes with no additional infrastructure, turbine hardware, or control software; it is simply the outcome of optimizing the turbine layout and yaw control together, resulting in millions of dollars of additional revenue for the wind power plants of the future.

Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares

AbstractWind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of and (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems.

Study on Mitigation of Wake Interference by Combined Control of Yaw Misalignment and Pitch

Yaw misalignment can make a wake steer, which is an effective method to increase the power of wind farms but it also increases the fatigue load of the turbines. In this paper, the combination of yaw offset and pitch control (CYMP) is studied to analyse the potential mitigation of wake, focusing on the wind velocity and turbulence of the wake distribution, power increment, and fatigue load reduction. The simulation case study shows that the method of CYMP can reduce the fatigue load by 10.29% and increase the total power by 1.7% compared with only wake steering in FAST.Fram. The Collaborative MPC (CoMPC) method based on CYMP is proposed to the real-time wake control in this research, which can increase power by more than 2% and reduce thrust by more than 4% than greedy control under 10 m/s turbulent wind.

Experimental and numerical study of the wake deflections of scaled vertical axis wind turbine models

Wake steering of vertical axis wind turbines (VAWTs) is investigated experimentally and numerically via stereoscopic particle image velocimetry and Reynolds averaged Navier-Stokes simulations. Three different blade pitch angles (-10°, 0°, 10°) of straight H-type VAWTs are adopted to deflect and deform the wake. The experimental results confirm the efficacy of blade pitching on the wake steering, and validate the simulation for both moderate and significant wake deflections. The simulation is then extended to full-scale VAWTs, exploring the wake deflection effects on the power performance of VAWT arrays. The effects of inter-turbine distances and pitching configurations are considered. With the upwind VAWT deflecting the wake, the overall power coefficient is increased by 41%.

Study on the yaw-based wake steering control considering dynamic flow characteristics for wind farm power improvement

Wake steering is one of the wind farm control strategies that can effectively reduce wake losses and increase power generation. Upstream wind turbines are set with yaw misalignment angles to deflect their wake away from downstream wind turbines, which will alleviate the wake effect. Currently, the yaw misalignment angles of the wind turbines are optimized through CFD or analytical wake models with a fixed inflow wind direction. However, wind turbines operate in rapidly changing wind directions where they cannot immediately track the optimized yaw misalignment angles owing to the slow yaw mechanism as expected. Therefore, a dynamic yaw control method is proposed in this study with the aim of balancing the energy gain and the number of yaw movements. Firstly, in order to improve efficiency, the yaw misalignment set points of wind turbines are optimized with different wind conditions based on the steady wake model. Then, we illustrate the procedure for determining the yaw misalignment angles for dynamic wind conditions. Finally, the performance of the yaw controller under the measured inflow condition is analyzed for the case of two-turbine array. It was found that the optimized yaw misalignment angles based on the steady model are applied to the dynamic control under changing wind direction, where the energy gain does not reach the estimated value using the static optimized control.

Smart cooperative control scheme for large-scale wind farms based on a double-layer machine learning framework

In the real-time cooperative control of large-scale wind farms, the simultaneous achievement of accuracy and efficiency by the optimization framework plays an indispensable role. This paper presents a new double-layer machine learning (ML) framework comprising an Artificial Neural Networks (ANN) yawed wake model and Bayesian ML algorithm to strike a desirable compromise between accuracy and efficiency. Given the control on the iteration number with the scale-up of the wind farm, a novel row-based control scheme is further put forward to improve the optimization rate by reasonably reducing the optimization parameters. Moreover, parametric analysis has been performed considering the wind distribution and layout configuration to explore its applicability compared with the general independent one. The study shows that the novel framework performs favorably in an accurate and efficient power prediction and optimization of the wind farm. The row-based control scheme can further improve the convergence rate of the double-layer optimization framework remarkably at the expense of a slight decrease in optimal power production. The divergence of the wind distribution can dwindle the power gain of the wake steering strategy and weaken the superiority of the row-based cooperative control scheme. The row-based cooperative control scheme is more applicable to the aligned layout than the staggered layout, and this advantage is enhanced with the increase of wind farm scale.

Data–Driven Wake Steering Control for a Simulated Wind Farm Model

Abstract: Upstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting.

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.

Physical Modelling of Tidal Stream Turbine Wake Structures under Yaw Conditions

Tidal stream turbines may operate under yawed conditions due to variability in ocean current directions. Insight into the wake structure of yawed turbines can be essential to ensure efficient tidal stream energy extraction, especially for turbine arrays where wake interactions emerge. We studied experimentally the effects of turbines operating under varying yaw conditions. Two scenarios, including a single turbine and a set of two turbines in alignment, were configured and compared. The turbine thrust force results confirmed that an increasing yaw angle results in a decrease in the turbine streamwise force and an increase in the turbine spanwise force. The velocity distribution from the single turbine scenario showed that the wake deflection and velocity deficit recovery rate increased at a rate proportional to the yaw angle. The two-turbine scenario results indicated that the deployment of an upstream non-yawed turbine significantly limited the downstream wake steering (i.e., the wake area behind the downstream turbine). Interestingly, a yawed downstream turbine was seen to influence the steering of both the upstream and the downstream wakes. These systematically derived data could be regarded as useful references for the numerical modelling and optimisation of large arrays.

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.

Wind Tunnel Testing of Combined Derating and Wake Steering

This paper describes an experimental assessment of the impact of combined derating and wake steering by yawing on the power output of a cluster of three wind turbines. The test is conducted in a boundary layer wind tunnel, and the flow control strategies are applied to the sole upstream machine. The experiment reveals a relationship, so far never observed experimentally, between the amount of yaw-induced power losses and the implemented level of derating. Furthermore, combining derating and yawing can only be beneficial, in terms of improved power production at the cluster level, for modest derating values.

Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark

Upstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained online to calculate yaw angles from operating data, including turbine status, using a hybrid model and learning-based active control method. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm, as opposed to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. Instead, the integration of learning strategy into the control design enables the creation of an active control scheme. The suggested methodology differs from solely learning-based approaches in that it doesn't call for a significant number of training samples, such as in model-free reinforcement learning. In actuality, by taking use of the model during backpropagation, the suggested approach learns more from each sample. Using a well-known and practical wind farm benchmark, results are reported for both standard (nominal) wake steering under operational conditions with all turbines and for faulty conditions.

Adjoint optimisation for wind farm flow control with a free-vortex wake model

Wind farm flow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm flows have been essential for exploring control strategies such as wake redirection and dynamic induction control. Free-vortex methods can provide a computationally efficient way to model wind turbine wake dynamics for control optimisation. We present a control-oriented free-vortex wake model of a 2D and 3D actuator disc to represent wind turbine wakes. The novel derivation of the discrete adjoint equations allows efficient gradient evaluation for gradient-based optimisation in an economic model-predictive control algorithm. Initial results are presented for mean power maximisation in a two-turbine case study. An induction control signal is found using the 2D model that is roughly periodic and supports previous results on dynamic induction control to stimulate wake mixing. The 3D model formulation effectively models a curled wake under yaw misalignment. Under time-varying wind direction, the optimisation finds solutions demonstrating both wake steering and a smooth transition to greedy control. The free-vortex wake model with gradient information shows potential for efficient optimisation and provides a promising way to further explore dynamic wind farm flow control.

Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling

Dynamic power control of wind farms (WFs) is necessary to provide automatic generation control (AGC) services for the power system. However, cooperative WF control for AGC remains a great challenge because of the nonlinear and high-dimensional nature of the wake flow dynamics. To address this challenge, this article proposes a model predictive control (MPC) framework with deep learning-based reduced-order modeling (ROM). Two novel neural network architectures are designed, which successfully formulate a WF ROM capturing the dominant wake steering dynamics in a computationally efficient manner. Compared to physical models, the data-driven ROM reduces the number of model states by orders of magnitude. Then, a novel WF AGC framework embedding the derived WF ROM is proposed. Thrust coefficient and yaw steering are both employed to optimize WF power tracking performance. Compared to prior WF AGC controllers, the dynamic yaw actuation is first optimized for AGC considering the wake steering dynamics. Case studies validate the effectiveness of the deep learning-based WF ROM at capturing the wake traveling dynamics. The WF controllers were stress-tested under time-varying inflow directions. The proposed MPC can react to different wind directions and generates higher-quality control performance than existing alternatives with extended trackable AGC range and better dynamic power tracking performance.

Multifidelity multiobjective optimization for wake-steering strategies

Abstract. Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. A large eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. Multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost only 30 % that of the single-fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

A time‐varying formulation of the curled wake model within the FAST.Farm framework

AbstractIn this article, we present a time‐varying formulation of the curled wake model that we implemented in FAST.Farm. The curled wake model, originally developed for steady‐state conditions, is used to produce realistic wake profiles behind a wind turbine in yawed (or skewed) conditions. We begin by introducing the key elements of the FAST.Farm framework. Then, after briefly summarizing the original wake dynamics formulation of FAST.Farm based on a polar wake profile, we present the new time‐varying formulation of the curled wake model, compare the two, and highlight the differences with the original curled wake model. After discussing some implementation details, we present different applications with increasing levels of complexity: single turbine with uniform and turbulent inflow, fixed and transient yaw, and multiple turbines. We verify our results using the original FAST.Farm implementation and large‐eddy simulations. The results with the new curled wake model are improved compared to the original implementation, as they include cross‐flow velocities and wake asymmetry. Yet, large‐eddy simulation results show a more pronounced lateral convection of the wake and a stronger concentration of vorticity at the top vortex. The new curled wake implementation in FAST.Farm should enable the calculation of not only generator power but also wind turbine structural loads for applications involving intentional or unintentional skewed flow and wind‐farm control involving wake steering.

Collective wind farm operation based on a predictive model increases utility-scale energy production

In wind farms, turbines are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. Here we operate a wind turbine array collectively to maximize array production through wake steering. We develop a physics-based, data-assisted flow control model to predict the power-maximizing control strategy. We first validate the model with a multi-month field experiment at a utility-scale wind farm. The model is able to predict the yaw-misalignment angles which maximize array power production within ± 5° for most wind directions (5–32% gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 3.0% ± 0.7% and 1.2% ± 0.4% for wind speeds between 6 m s−1 and 8 m s−1  and all wind speeds, respectively. The predictive model can enable a wider adoption of collective wind farm operation. Individual operation of turbines in wind farms results in energy losses from wake interactions. Here Howland et al. report on an experimentally validated model to implement collective operation of turbines, which increases the farm’s energy production.

Vertical wake deflection for floating wind turbines by differential ballast control

Abstract. This paper presents a feasibility analysis of vertical wake steering for floating turbines by differential ballast control. This new concept is based on the idea of pitching the floater with respect to the water surface, thereby achieving a desired tilt of the turbine rotor disk. The pitch attitude is controlled by moving water ballast among the columns of the floater. This study considers the application of differential ballast control to a conceptual 10 MW wind turbine installed on two platforms, differing in size, weight, and geometry. The analysis considers the following: (a) the aerodynamic effects caused by rotor tilt on the power capture of the wake-steering turbine and at various downstream distances in its wake; (b) the effects of tilting on fatigue and ultimate loads, limitedly to one of the two turbine-platform layouts; and (c) for both configurations, the necessary amount of water movement, the time to achieve a desired attitude, and the associated energy expenditure. Results indicate that – in accordance with previous research – steering the wake towards the sea surface leads to larger power gains than steering it towards the sky. Limitedly to the structural analysis conducted on one of the turbine-platform configurations, it appears that these gains can be obtained with only minor effects on loads, assuming a cautious application of vertical steering only in benign ambient conditions. Additionally, it is found that rotor tilt can be achieved on the order of minutes for the lighter of the two configurations, with reasonable water ballast movements. Although the analysis is preliminary and limited to the specific cases considered here, results seem to suggest that the concept is not unrealistic and should be further investigated as a possible means to achieve variable tilt control for vertical wake steering in floating turbines.