Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “wake steering”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Loads assessment of a fixed-bottom offshore wind farm with wake steering

Wake steering via deliberate yaw offset is an emerging wind farm control technique that has the potential to mitigate wake losses and further increase wind farm energy yield. The loads impact of this technique has been studied, but there is limited insight into wind-farm-wide impacts of wake steering. Understanding such impacts is crucial to determining the feasibility of using wake steering in commercial wind farms. To that end, this work investigates the impacts of wake steering on the loads of all turbine components across all turbines in a wind farm operating under a broad set of inflow conditions, including inflow velocity, shear exponent, turbulence class, and inflow angle. This was done by performing FAST.Farm simulations of a 12-turbine wind farm array, excerpted from a larger hypothetical wind farm. The International Energy Agency Wind 15-MW reference wind turbine was modeled atop a monopile substructure, an open-source model that closely approximates the properties of similar commercial options. Wake steering was included via yaw offsets that were computed using an offline optimization with the National Renewable Energy Laboratory tool FLORIS. For each inflow case, the 12-turbine array was simulated with and without wake steering. Results were compared in terms of time-averaged means, standard deviations, ultimate loads, and damage-equivalent loads. The findings show that because wake steering is generally applied at rated wind speeds and below, it is unlikely to drive ultimate loads. For fatigue loads, wake steering does increase the overall fatigue accumulation for some load channels, such as blade-root and shaft bending. This is to be expected when overall power yield increases but may cause the damage accumulation to be more uniform throughout the array. The significance of the added fatigue loading is dependent on how frequent wake steering is utilized in the overall set of inflow conditions across the wind rose.

17 WIND ENERGY↗
DOE OSTI2025

Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering

Wake steering strategies are employed to increase the overall power production of wind farms by deflecting wakes of upstream turbines away from downstream ones. The gain in net power comes at the expense of increased fatigue loads experienced by downstream turbines. In this work we investigate performance and fatigue loading characteristics of a small farm consisting of five aligned International Energy Agency Wind Technology Collaboration Programme 15 MW wind turbines. A parametric study is performed where, for every wind direction from −20 to 20°, the yaw misalignment angle varies from −25 to 25°. This setup allows us to investigate asymmetries and identify optimal conditions for a given wind direction. In general, we find that positive yaw configurations are preferred and that yaw configurations that result in attractive power differences when compared to a baseline no-yaw scenario (25 %) come with significant increase in fatigue loading (we use the standard deviation and damage-equivalent load (DEL) of the blade-root, low-speed shaft, and tower-base moments as proxies for fatigue load). We find that for any given positive wind inflow angle, yaw angles between −2.5 and 15° yield power differences of 10 %–20 % over a no-yaw baseline, and positive yaw is preferred because of lower fatigue loading. For any given negative wind inflow angles, positive yaw also results in lower magnitudes of standard deviation and DEL for the channels investigated. A small power loss of up to 2 % is observed for some positive yaw angles under negative wind directions (as compared to symmetric negative yaw and positive wind cases), but improvements in terms of loads exceed 25 % and may be enough to justify a positive yaw configuration under negative winds as well. We show that such behavior can be explained by partial waking and the direction of the rotation of the rotor.

17 WIND ENERGY↗
DOE OSTI2021

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6% for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3%. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

17 WIND ENERGY↗
DOE OSTI2022

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.

17 WIND ENERGY↗
DOE OSTI2026

Assessment of Consensus and Wake Steering Wind Farm Control for the American WAKE ExperimeNt (AWAKEN)

As part of the AmericanWAKE ExperimeNt (AWAKEN), a wind farm control experiment is being conducted at the King Plains wind plant in northern Oklahoma from May 2024 to summer 2025. Two types of wind farm control are being evaluated: 1) wake steering, in which upstream wind turbines are misaligned relative to the wind direction to deflect their wakes away from downstream turbines and increase total wind plant power, and 2) consensus yaw control, whereby each turbine's yaw position is controlled to track a "consensus" weighted average of the wind directions measured at neighboring turbines rather than the turbine's own nacelle wind direction measurement. By replacing the noisy wind direction measured by an individual turbine with the smoother, more slowly varying consensus wind direction, consensus yaw control is intended to reduce yaw activity and increase power capture by improving yaw alignment. To help balance the potential increase in yaw activity for the turbines implementing wake steering, they are also operated using consensus yaw control. In this presentation we highlight the impacts of consensus yaw control and wake steering on both energy production and yaw travel at the wind plant. Results show that the change in energy from wake steering is minor overall, but significant increases in energy are observed for closely spaced turbines. Further, larger increases in energy occur during low turbulence periods. The impact of consensus yaw control on energy production is currently inconclusive, with some energy gains measured for some turbines and losses measured for others. Lastly, consensus yaw control was found to reduce yaw travel significantly, even when combined with wake steering.

17 WIND ENERGY↗
DOE OSTI2020

Field Validation of Wake Steering Control with Wind Direction Variability

Wake steering is a wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes the increase in power production from wake-steering to be less than predicted by steady-state models, but more robust wake-steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two different 2-turbine wake-steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted, assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake-steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗
DOE OSTI2022

Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thereby increasing net energy production. But research suggests that the effectiveness of wake steering strongly depends on atmospheric conditions such as stability. In this paper, we investigate results from a two-turbine wake-steering experiment at a commercial wind plant to assess the impact of stability and five other atmospheric variables on wake-steering performance. Specifically, for different atmospheric condition bins we compare the ability of the controller to achieve the intended yaw offsets, the power gain from wake steering, and the reduction in wake losses. Further, we analyze wake-steering performance as a function of wind speed to eliminate the confounding impact of different wind speed distributions in different atmospheric conditions. Overall, we find that wind direction standard deviation is the best predictor of wake-steering performance, followed by turbulence intensity and turbulent kinetic energy. The results suggest the importance of adapting wake-steering control strategies to different atmospheric conditions.

17 WIND ENERGY↗
DOE OSTI2020

Field Validation of Wake Steering Control with Wind Direction Variability: Preprint

Wake steering is a type of wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes wake steering controllers to increase power production less than predicted by steady state models, but more robust wake steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two 2-turbine wake steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗
DOE OSTI2022

Chapter 38: Aerodynamics of Wake Steering

This chapter discusses the mechanisms that enable wake steering within a wind farm with a focus on wake steering performed using yaw misaligned turbines as this is the most popular approach to wake steering, although there are others. Wake steering is a type of wind farm control in which wind turbines in a wind farm operate with an intentional yaw misalignment to mitigate the effects of its wake on downstream turbines in order to increase overall combined wind farm energy production. This chapter goes into detail regarding the dominant aerodynamic characteristics that are present when a turbine operates in yaw misaligned conditions and suggests analytical models that can capture these effects. A detailed analysis of large-scale flow structures generated in wind farm control through yaw misalignment is presented. A collection of counter-rotating vortices, produced from a misaligned turbine, deforms the shape of the wake and produces asymmetric effects with oppositely signed yaw angles. These vortices generated by an upstream misaligned turbine can also deflect wakes of downstream non-misaligned turbines. This chapter also addresses the importance of modeling these counter-rotating vortices in analytical models for wind farm control design and for accurately quantifying the impacts of wake steering on gains in power production in larger wind farms.

modeling↗
DOE OSTI2023

Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model

Abstract. Wind farm design and analysis heavily rely on computationally efficient engineering models that are evaluated many times to find an optimal solution. A recent article compared the state-of-the-art Gauss-curl hybrid (GCH) model to historical data of three offshore wind farms. Two points of model discrepancy were identified therein: poor wake predictions for turbines experiencing a lot of wakes and wake interactions between two turbines over long distances. The present article addresses those two concerns and presents the cumulative-curl (CC) model. Comparison of the CC model to high-fidelity simulation data and historical data of three offshore wind farms confirms the improved accuracy of the CC model over the GCH model in situations with large wake losses and wake recovery over large inter-turbine distances. Additionally, the CC model performs comparably to the GCH model for single- and fewer-turbine wake interactions, which were already accurately modeled. Lastly, the CC model has been implemented in a vectorized form, greatly reducing the computation time for many wind conditions. The CC model now enables reliable simulation studies for both small and large offshore wind farms at a low computational cost, thereby making it an ideal candidate for wake-steering optimization and layout optimization.

17 WIND ENERGY↗
DOE OSTI2021

Wake Steering Wind Farm Control With Preview Wind Direction Information: Preprint

Wake steering is a wind farm control strategy in which upstream turbines operate with a yaw misalignment to deflect their wakes away from downstream turbines, yielding a net power gain for the wind plant. But the inability of wake steering controllers to perfectly track the wind direction leads to suboptimal performance. In this paper, we propose the use of preview wind direction measurements upstream of the turbine to improve controller performance by anticipating wind direction changes. Further, data from an operational wind plant are used to determine realistic preview measurement accuracy. Using the FLORIS engineering wind farm control tool, we compare the performance of standard and preview-enabled baseline and wake steering control for a two-turbine array during below-rated operation. Assuming perfect preview information, preview-based wake steering increases energy production by the equivalent of 9.5% of the baseline wake losses, compared to a wake loss recovery of 5.6% with standard wake steering. However, when realistic measurement accuracy is included, the preview-based controller provides no advantage over standard control, motivating the need for more sophisticated control and wind direction forecasting strategies.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Evaluation of the potential for wake steering for U.S. land-based wind power plants

The wind power plant-wide control strategy known as wake steering involves the misalignment of upstream turbines with the wind direction to deflect wakes away from downstream turbines, increasing net wind plant power production. In this paper, we evaluate the potential of wake steering for U.S. land-based wind power plants. First, we outline a method for simulating and optimizing wake steering control for existing wind plants by combining the flow redirection and induction in steady state wake steering engineering model with the U.S. Wind Turbine Database and Wind Integration National Dataset Toolkit wind resource dataset. Next, to better understand the potential benefits of wake steering beyond those for existing wind plants, we evaluate the relative impacts of turbine specific power, turbine spacing, and mean wind speed on energy gain and levelized cost of energy (LCOE) using a model land-based wind power plant. For a subset of 60 existing wind plants, assuming a constant turbulence intensity of 8%, wake steering was found to yield an average annual energy production (AEP) gain of 0.80%, equivalent to recovering 13.85% of baseline wake losses. Further, we present a linear approximation between baseline wake losses and AEP gains that can be used to estimate wake steering gains for other wind power plants. Highlighting additional benefits of wake steering, for the model wind power plant we found that energy gains from wake steering enabled an approximate 30% reduction in turbine spacing while keeping LCOE constant.

17 WIND ENERGY↗
DOE OSTI2020

Wake steering optimization under uncertainty

Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertainties in many aspects of the wake steering problem. For example, infield sensors do not give perfect information, and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of independent uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and it is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. When maximizing expected power production, the OUU strategy also tends to favor smaller yaw angles, which have been shown in previous work to reduce turbine loading. Ultimately, the plant-level wake steering strategy formulated using an OUU approach yields 0.48?% more expected annual energy production for the 11-turbine wind plant than a strategy that neglects uncertainty when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, but it also reduces risk by prescribing strategies that call for less extreme yaw angles.

17 WIND ENERGY↗
DOE OSTI2024

Measuring the Effects of Wake Steering With Nacelle-Mounted Scanning Lidar

In large wind plants, wakes from upwind turbines affect downwind turbines by reducing wind speeds and increasing turbulence. Wake steering may mitigate this effect by deflecting the wakes of upwind turbines away from downwind units. Here, we characterize the impact of wakes from yawed turbines at a commercial-scale wind plant under varying atmospheric and turbine operating conditions. Six months of nacelle-based lidar measurements were collected as part of a field campaign in 2019-2020 in the northern US Great Plains to test the effectiveness of wake steering. We separate these lidar scans by atmospheric stability and turbine operating condition to summarize how yawed wind turbine wakes vary with these input parameters in the atmospheric boundary layer. We summarize the impact of wake steering on various wake characteristics including velocity deficit, wake width, and wake center as retrieved from these lidar data. Yawed wakes have significantly different centerline characteristics compared to unyawed wakes, with large regions of meander in the mid wake region. Yawed wakes are also deflected farther than unyawed wakes in less turbulent conditions with velocity deficits persisting further downstream in stable atmospheric conditions. Overall, yawed wind turbine wakes are larger and wider in lower wind speed environments, and yawed wakes are deflected farther in less turbulent conditions, suggesting that wake steering is most effective in stable atmospheric stratification.

17 WIND ENERGY↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
DOE OSTI2024

Coupled modeling of wake steering and platform offsets for floating wind arrays

Wake effects are a key challenge in the design and analysis of wind farms. For floating wind farms, the platforms offset under the aerodynamic loading of the turbine and are constrained by mooring systems that can vary significantly in allowable offsets. When considering wake steering, the crosswind offset of the turbine can counteract the lateral deflection of the wake. This work presents a tool to efficiently model the coupled impacts of wake steering and platform offsets for floating wind farms. The tool relies on the frequency-domain wind farm model RAFT and the steady-state wake model FLORIS. A verification with FAST.Farm is presented, then the tool is applied to a simple two-turbine case study. A range of mooring systems with increasing platform offsets and varied yaw misalignment angles are considered while comparing the impact on turbine power. Additional sensitivities to turbine spacing and mooring system orientation are explored. The results show that there is a least-optimal watch circle width for downwind turbine power production that varies with yaw misalignment angle and turbine spacing. Additionally, the turbine offsets under yaw-misaligned conditions vary significantly depending on mooring system orientation relative to the rotor plane, which in turn impacts the optimal misalignment angle. These results highlight the importance of including floating platform offsets and mooring systems in the evaluation of wake steering strategies for floating wind arrays.

17 WIND ENERGY↗
DOE OSTI2022

Fast yaw optimization for wind plant wake steering using Boolean yaw angles

Abstract. In wind plants, turbines can be yawed into the wind to steer their wakes away from downstream turbines and achieve an overall increase in plant power. Mathematical optimization is typically used to determine the best yaw angles at which to operate the turbines in a plant. In this paper, we present a new heuristic to rapidly determine the yaw angles in a wind plant. In this method, we define the turbine yaw angles as Boolean – either yawed at a predefined angle or nonyawed – as opposed to the typical methods of defining yaw angles as continuous or with fine discretizations. We then optimize which turbines should be yawed with an algorithm that sweeps through the turbines from the most upstream to the most downstream. We demonstrate that our new Boolean optimization method can find turbine yaw angles that perform well compared to a traditionally used gradient-based optimizer for which the yaw angles are defined as continuous. There is less than 0.6 % difference in the optimized power between the two optimization methods for randomly placed turbine layouts and less than a 0.6 % difference in the optimal annual energy production between the two optimization methods for a real wind farm. Additionally, we show that our new method is much more computationally efficient than the traditional method. For plants with nonzero optimal yaw angles, our new method is generally able to solve for the turbine yaw angles 50–150 times faster, and in some extreme cases up to 500 times faster, than the traditional method.

17 WIND ENERGY↗
DOE OSTI2021

Loads Response That is Due to Wake Steering on a Pair of Utility-Scale Wind Turbines

The goal of this report is to present the resulting turbine loads of a full-scale wind turbine wake steering field campaign. In this experiment, wake steering controls were applied to an upwind turbine (T2) and the downwind turbine (T3) was instrumented to measure mechanical loads. The test subjects (T2 & T3) were GE 1.5SLE CWE turbines located on a wind farm with a prevailing wind direction coming from the northwest. The data collection strategy involved toggling the wake steering controls of T2 between on and off. This resulted in two databases (baseline and wake steered) with similar turbulence intensities. Valid data was extracted and processed. The analysis involved scaling data to engineering units, applying coordinate transformations where applicable, calculating 10-minutes statistics, and calculating damage equivalent loads (DELs) to assess fatigue. Figures are shown as statistics and DELs binned by wind speed with supplemental scatter plots provided in the appendices. Results between the baseline and wake steered databases were compared. Overall, the binned statistics showed minimal differences in loading between the two cases. However, the DELs of the wake steered case were observed to be consistently smaller than the baseline for most of the turbine components. A potential for future work was recognized based on the findings of this report. Some areas of further study include an analysis for more granular loads sensitivity to different yaw offset angles and rotor wake overlap, an in-depth comparison of loads between the upwind and downwind turbines, and further fatigue analysis to quantify differences observed in this experiment.

17 WIND ENERGY↗