Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1427-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-1427-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Nov 2021
Research article |
| 12 Nov 2021
| 12 Nov 2021
Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Paul Fleming
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Nicolas Girard
ENGIE Digital, 6 rue Alexander Fleming, 69007 Lyon, France
Lucas Alloin
ENGIE Green, 6 rue Alexander Fleming, 69007 Lyon, France
Emma Godefroy
ENGIE Green, 6 rue Alexander Fleming, 69007 Lyon, France
Thomas Duc
ENGIE Green, 6 rue Alexander Fleming, 69007 Lyon, France
Related authors
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nathaniel deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci., 10, 1737–1762, https://doi.org/10.5194/wes-10-1737-2025, https://doi.org/10.5194/wes-10-1737-2025, 2025
Short summary
Short summary
This paper presents one half of a companion paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for a wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
Joeri A. Frederik, Eric Simley, Kenneth A. Brown, Gopal R. Yalla, Lawrence C. Cheung, and Paul A. Fleming
Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, https://doi.org/10.5194/wes-10-755-2025, 2025
Short summary
Short summary
In this paper, we present results from advanced computer simulations to determine the effects of applying different control strategies to a small wind farm. We show that when there is variability in wind direction over height, steering the wake of a turbine away from other turbines is the most effective strategy. When this variability is not present, actively changing the pitch angle of the blades to increase turbulence in the wake could be more effective.
Eric Simley, Dev Millstein, Seongeun Jeong, and Paul Fleming
Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, https://doi.org/10.5194/wes-9-219-2024, 2024
Short summary
Short summary
Wake steering is a wind farm control technology in which turbines are misaligned with the wind to deflect their wakes away from downstream turbines, increasing total power production. In this paper, we use a wind farm control model and historical electricity prices to assess the potential increase in market value from wake steering for 15 US wind plants. For most plants, we find that the relative increase in revenue from wake steering exceeds the relative increase in energy production.
Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 8, 1693–1710, https://doi.org/10.5194/wes-8-1693-2023, https://doi.org/10.5194/wes-8-1693-2023, 2023
Short summary
Short summary
Unexpected wind direction changes are undesirable, especially when performing wake steering. This study explores whether the yaw controller can benefit from accessing wind direction information before a change reaches the turbine. Results from two models with different fidelities demonstrate that wake steering can indeed benefit from preview information.
Paul Fleming, Michael Sinner, Tom Young, Marine Lannic, Jennifer King, Eric Simley, and Bart Doekemeijer
Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, https://doi.org/10.5194/wes-6-1521-2021, 2021
Short summary
Short summary
The paper presents a new validation campaign of wake steering at a commercial wind farm. The campaign uses fixed yaw offset positions, rather than a table of optimal yaw offsets dependent on wind direction, to enable comparison with engineering models of wake steering. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions.
Liang Dong, Wai Hou Lio, and Eric Simley
Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, https://doi.org/10.5194/wes-6-1491-2021, 2021
Short summary
Short summary
This paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of lidar-assisted control (LAC) in wind turbine design. The value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
Short summary
Short summary
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
Short summary
Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nathaniel deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci., 10, 1737–1762, https://doi.org/10.5194/wes-10-1737-2025, https://doi.org/10.5194/wes-10-1737-2025, 2025
Short summary
Short summary
This paper presents one half of a companion paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for a wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Preprint under review for WES
Short summary
Short summary
This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Joeri A. Frederik, Eric Simley, Kenneth A. Brown, Gopal R. Yalla, Lawrence C. Cheung, and Paul A. Fleming
Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, https://doi.org/10.5194/wes-10-755-2025, 2025
Short summary
Short summary
In this paper, we present results from advanced computer simulations to determine the effects of applying different control strategies to a small wind farm. We show that when there is variability in wind direction over height, steering the wake of a turbine away from other turbines is the most effective strategy. When this variability is not present, actively changing the pitch angle of the blades to increase turbulence in the wake could be more effective.
Eric Simley, Dev Millstein, Seongeun Jeong, and Paul Fleming
Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, https://doi.org/10.5194/wes-9-219-2024, 2024
Short summary
Short summary
Wake steering is a wind farm control technology in which turbines are misaligned with the wind to deflect their wakes away from downstream turbines, increasing total power production. In this paper, we use a wind farm control model and historical electricity prices to assess the potential increase in market value from wake steering for 15 US wind plants. For most plants, we find that the relative increase in revenue from wake steering exceeds the relative increase in energy production.
Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 8, 1693–1710, https://doi.org/10.5194/wes-8-1693-2023, https://doi.org/10.5194/wes-8-1693-2023, 2023
Short summary
Short summary
Unexpected wind direction changes are undesirable, especially when performing wake steering. This study explores whether the yaw controller can benefit from accessing wind direction information before a change reaches the turbine. Results from two models with different fidelities demonstrate that wake steering can indeed benefit from preview information.
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
Short summary
Short summary
Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
Short summary
Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
Short summary
Short summary
We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022, https://doi.org/10.5194/wes-7-1137-2022, 2022
Short summary
Short summary
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022, https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
Short summary
In wind plants, turbines can be yawed to steer their wakes away from downstream turbines and achieve an increase in plant power. The yaw angles become expensive to solve for in large farms. This paper presents a new method to solve for the optimal turbine yaw angles in a wind plant. The yaw angles are defined as Boolean variables – each turbine is either yawed or nonyawed. With this formulation, most of the gains from wake steering can be reached with a large reduction in computational expense.
Paul Fleming, Michael Sinner, Tom Young, Marine Lannic, Jennifer King, Eric Simley, and Bart Doekemeijer
Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, https://doi.org/10.5194/wes-6-1521-2021, 2021
Short summary
Short summary
The paper presents a new validation campaign of wake steering at a commercial wind farm. The campaign uses fixed yaw offset positions, rather than a table of optimal yaw offsets dependent on wind direction, to enable comparison with engineering models of wake steering. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions.
Liang Dong, Wai Hou Lio, and Eric Simley
Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, https://doi.org/10.5194/wes-6-1491-2021, 2021
Short summary
Short summary
This paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of lidar-assisted control (LAC) in wind turbine design. The value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
Short summary
Short summary
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Stefano Macrí, Sandrine Aubrun, Annie Leroy, and Nicolas Girard
Wind Energ. Sci., 6, 585–599, https://doi.org/10.5194/wes-6-585-2021, https://doi.org/10.5194/wes-6-585-2021, 2021
Short summary
Short summary
This paper investigates the effect of misaligning a wind turbine on its wake deviation response and on the global load variation of a downstream wind turbine during a positive and negative yaw maneuver, representing a misalignment–realignment scenario. Yaw maneuvers could be used to voluntarily misalign wind turbines when wake steering control is targeted. The aim of this wind farm control strategy is to optimize the overall production of the wind farm and its lifetime.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
Short summary
Short summary
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
Short summary
Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
Short summary
Short summary
We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Cited articles
Adaramola, M. S. and Krogstad, P. Å.: Experimental investigation of wake
effects on wind turbine performance, Renew. Energ., 36, 2078–2086, 2011. a
Ahmad, T., Coupiac, O., Petit, A., Guignard, S., Girard, N., Kazemtabrizi, B.,
and Matthews, P.: Field Implementation and Trial of Coordinated Control of
Wind Farms, IEEE T. Sustain. Energ., 9, 1169–1176, https://doi.org/10.1109/TSTE.2017.2774508, 2017. a
Annoni, J., Bay, C., Johnson, K., Dall'Anese, E., Quon, E., Kemper, T., and Fleming, P.: Wind direction estimation using SCADA data with consensus-based optimization, Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, 2019. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine
wakes, Renew. Energ., 70, 116–123, 2014. a
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind
turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541,
2016. a
Bastankhah, M. and Porté-Agel, F.: Wind farm power optimization via yaw angle
control: A wind tunnel study, J. Renew. Sustain. Ener.,
11, 023301, https://doi.org/10.1063/1.5077038, 2019. a
Boersma, S., Doekemeijer, B. M., Gebraad, P. M. O., Fleming, P. A., Annoni, J.,
Scholbrock, A. K., Frederik, J. A., and Wingerden, J. W. V.: A tutorial on
control-oriented modeling and control of wind farms, in: Proc. American
Control Conference, Seattle, WA, USA, 1–18, 2017. a
Bossanyi, E.: Combining induction control and wake steering for wind farm
energy and fatigue loads optimisation, J. Phys. Conf. Ser.,
1037, 032011, https://doi.org/10.1088/1742-6596/1037/3/032011, 2018. a, b
Bossanyi, E.: Optimising yaw control at wind farm level, J. Phys.
Conf. Ser., 1222, 012023, https://doi.org/10.1088/1742-6596/1222/1/012023,
2019. a
Campagnolo, F., Petrović, V., Bottasso, C. L., and Croce, A.: Wind tunnel
testing of wake control strategies, in: Proc. American Control
Conference, Boston, MA, USA, 513–518, 2016. a
Cossu, C.: Wake redirection at higher axial induction, Wind Energ. Sci., 6, 377–388, https://doi.org/10.5194/wes-6-377-2021, 2021. a
Dahlberg, J. and Medici, D.: Potential improvement of wind turbine array
efficiency by active wake control, in: Proc. European Wind Energy
Conference, Madrid, Spain, 16–19 June 2003. a
Damiani, R., Dana, S., Annoni, J., Fleming, P., Roadman, J., van Dam, J., and Dykes, K.: Assessment of wind turbine component loads under yaw-offset conditions, Wind Energ. Sci., 3, 173–189, https://doi.org/10.5194/wes-3-173-2018, 2018. a
Dekking, F. M., Kraaikamp, C., Lopuhaä, H. P., and Meester, L. E.: A Modern
Introduction to Probability and Statistics, Springer, London, 2005. a
Doekemeijer, B. M., van der Hoek, D., and van Wingerden, J.-W.: Closed-loop
model-based wind farm control using FLORIS under time-varying inflow
conditions, Renew. Energ., 156, 719–730,
https://doi.org/10.1016/j.renene.2020.04.007, 2020. a
Doekemeijer, B. M., Kern, S., Maturu, S., Kanev, S., Salbert, B., Schreiber, J., Campagnolo, F., Bottasso, C. L., Schuler, S., Wilts, F., Neumann, T., Potenza, G., Calabretta, F., Fioretti, F., and van Wingerden, J.-W.: Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy, Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, 2021. a, b, c
Duc, T., Coupiac, O., Girard, N., Giebel, G., and Göçmen, T.: Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, Wind Energ. Sci., 4, 287–302, https://doi.org/10.5194/wes-4-287-2019, 2019. a
Fleming, P., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z., Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering at an offshore wind farm, Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, 2017. a, b
Fleming, P., Annoni, J., Churchfield, M., Martinez-Tossas, L. A., Gruchalla, K., Lawson, M., and Moriarty, P.: A simulation study demonstrating the importance of large-scale trailing vortices in wake steering, Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, 2018. a
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019. a, b, c, d
Fleming, P., King, J., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Jager, D., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2, Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, 2020. a, b, c, d, e, f
Frost, C. and Thompson, S. G.: Correcting for regression dilution bias:
Comparison of methods for a single predictor variable, J. Roy.
Stat. Soc. A Sta., 163, 173–189, 2000. a
Gebraad, P., Teeuwisse, F., Wingerden, J., Fleming, P. A., Ruben, S., Marden,
J., and Pao, L.: Wind plant power optimization through yaw control using a
parametric model for wake effects – a CFD simulation study, Wind Energy,
19, 95–114, https://doi.org/10.1002/we.1822, 2016. a, b
Gebraad, P., Thomas, J. J., Ning, A., Fleming, P., and Dykes, K.: Maximization
of the annual energy production of wind power plants by optimization of
layout and yaw-based wake control, Wind Energy, 20, 97–107, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J. P., Peubey, C., Radu, R., Schepers, D., Simmons, A. J., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J.-R., Bonavita, M., De Chiara, G. and
Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R. G., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hogan, R. J., Holm, E. V., Janisková, M., Keeley, S. P. E., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.-N.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc.,
146, 1999–2049, 2020. a
Howland, M. F., Lele, S. K., and Dabiri, J. O.: Wind farm power optimization
through wake steering, P. Natl. Acad. Sci., 116,
14495–14500, https://doi.org/10.1073/pnas.1903680116, 2019. a, b
Howland, M. F., González, C. M., Martínez, J. J. P., Quesada, J. B.,
Larrañaga, F. P., Yadav, N. K., Chawla, J. S., and Dabiri, J. O.: Influence
of atmospheric conditions on the power production of utility-scale wind
turbines in yaw misalignment, J. Renew. Sustain. Ener.,
12, 063307, https://doi.org/10.1063/5.0023746, 2020. a, b
Kanev, S.: Dynamic wake steering and its impact on wind farm power production
and yaw actuator duty, Renew. Energ., 146, 9–15,
https://doi.org/10.1016/j.renene.2019.06.122, 2020. a
Kanev, S. K., Savenije, F. J., and Engels, W. P.: Active wake control: An
approach to optimize the lifetime operation of wind farms, Wind Energy, 21,
488–501, https://doi.org/10.1002/we.2173, 2018. a
Kelberlau, F. and Mann, J.: Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar, Wind Energ. Sci., 5, 519–541, https://doi.org/10.5194/wes-5-519-2020, 2020. a
King, J., Fleming, P., King, R., Martínez-Tossas, L. A., Bay, C. J., Mudafort, R., and Simley, E.: Control-oriented model for secondary effects of wake steering, Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, 2021. a, b
Kragh, K. A. and Hansen, M. H.: Potential of power gain with improved yaw
alignment, Wind Energy, 18, 979–989, https://doi.org/10.1002/we.1739,
2015. a
Liew, J., Urbán, A. M., and Andersen, S. J.: Analytical model for the power–yaw sensitivity of wind turbines operating in full wake, Wind Energ. Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, 2020. a
López, B., Guggeri, A., Draper, M., and Campagnolo, F.: Wake steering
strategies for combined power increase and fatigue damage mitigation: an
LES study, J. Phys. Conf. Ser., 1618, 022067,
https://doi.org/10.1088/1742-6596/1618/2/022067, 2020. a
Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J.: The aerodynamics of the curled wake: a simplified model in view of flow control, Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, 2019. a
Medici, D.: Experimental studies of wind turbine wakes : power optimisation and
meandering, PhD Thesis, KTH Mechanics, Royal Institute of Technology, Stockholm, Sweden, 2005. a
Moriarty, P., Hamilton, N., Debnath, M., Herges, T., Isom, B., Lundquist,
J. K., Mainaci, D., Naughton, B., Pauly, R., Roadman, J., Shaw, W., van Dam,
J., and Wharton, S.: American WAKE experimeNt (AWAKEN),
NREL/TP-5000-75789, Tech. rep., National Renewable Energy Laboratory,
Golden, CO, 2020. a
Niayifar, A. and Porté-Agel, F.: A new analytical model for wind farm power
prediction, J. Phys. Conf. Ser., 625,
012039, https://doi.org/10.1088/1742-6596/625/1/012039, 2015. a, b
Nouri, R., Vasel-Be-Hagh, A., and Archer, C. L.: The Coriolis force and the
direction of rotation of the blades significantly affect the wake of wind
turbines, Appl. Energ., 277, 115511,
https://doi.org/10.1016/j.apenergy.2020.115511, 2020. a
Perr-Sauer, J., Optis, M., Fields, J. M., Bodini, N., Lee, J. C., Todd, A.,
Simley, E., Hammond, R., Phillips, C., Lunacek, M., Kemper, T., Williams, L.,
Craig, A., Agarwal, N., Sheng, S., and Meissner, J.: OpenOA: An Open-Source
Codebase For Operational Analysis of Wind Farms, Journal of Open Source
Software, 6, 2171, https://doi.org/10.21105/joss.02171, 2021. a
Quick, J., Annoni, J., King, R., Dykes, K., Fleming, P., and Ning, A.:
Optimization under uncertainty for wake steering strategies, J.
Phys. Conf. Ser., 854, 012036,
https://doi.org/10.1088/1742-6596/854/1/012036, 2017. a
Quick, J., King, J., King, R. N., Hamlington, P. E., and Dykes, K.: Wake steering optimization under uncertainty, Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, 2020. a, b, c
Rott, A., Doekemeijer, B., Seifert, J. K., van Wingerden, J.-W., and Kühn, M.: Robust active wake control in consideration of wind direction variability and uncertainty, Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, 2018. a, b
Simley, E., Fleming, P., and King, J.: Field Validation of Wake Steering
Control with Wind Direction Variability, J. Phys. Conf.
Ser., 1452, 012012, https://doi.org/10.1088/1742-6596/1452/1/012012,
2020a. a, b
van der Hoek, D., Kanev, S., Allin, J., Bieniek, D., and Mittelmeier, N.:
Effects of axial induction control on wind farm energy production – A field
test, Renew. Energ., 140, 994–1003, 2019. a
Vollmer, L., Steinfeld, G., Heinemann, D., and Kühn, M.: Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study, Wind Energ. Sci., 1, 129–141, https://doi.org/10.5194/wes-1-129-2016, 2016. a
Short summary
Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their low-velocity wakes away from downstream turbines, increasing overall power production. Here, we present results from a two-turbine wake-steering experiment at a commercial wind plant. By analyzing the wind speed dependence of wake steering, we find that the energy gained tends to increase for higher wind speeds because of both the wind conditions and turbine operation.
Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned...
Altmetrics
Final-revised paper
Preprint