Articles | Volume 8, issue 12
https://doi.org/10.5194/wes-8-1909-2023
© Author(s) 2023. 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-8-1909-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2023
Research article |
| 20 Dec 2023
| 20 Dec 2023
Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects
Maarten J. van den Broek
CORRESPONDING AUTHOR
Delft Center for Systems and Control, TU Delft, Mekelweg 2, 2628CD Delft, the Netherlands
Delphine De Tavernier
Department of Flow Physics and Technology, Wind Energy, TU Delft, Kluyverweg 1, 2629HS Delft, the Netherlands
Paul Hulsman
ForWind – Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
Daan van der Hoek
Delft Center for Systems and Control, TU Delft, Mekelweg 2, 2628CD Delft, the Netherlands
Benjamin Sanderse
Scientific Computing, CWI, P.O. Box 94079, 1090GB Amsterdam, the Netherlands
Jan-Willem van Wingerden
Delft Center for Systems and Control, TU Delft, Mekelweg 2, 2628CD Delft, the Netherlands
Related authors
Maarten J. van den Broek, Marcus Becker, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, https://doi.org/10.5194/wes-9-721-2024, 2024
Short summary
Short summary
Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
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.
Maria Cristina Vitulano, Delphine Anne Marie De Tavernier, Giuliano De Stefano, and Dominic Alexander von Terzi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-47, https://doi.org/10.5194/wes-2024-47, 2024
Preprint under review for WES
Short summary
Short summary
Next-generation wind turbines are the largest rotating machines ever built, experiencing local flow Mach where the incompressibility assumption is violated, and even transonic flow can occur. This study assesses the transonic features over the FFA-W3-211 wind turbine tip airfoil for selected industrial test cases, defines the subsonic-supersonic flow threshold, and evaluates the Reynolds number effects on transonic flow occurrence. Shock wave occurrence is also depicted.
Matteo Baricchio, Pieter M. O. Gebraad, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-50, https://doi.org/10.5194/wes-2024-50, 2024
Preprint under review for WES
Short summary
Short summary
Wake steering can be integrated within the wind farm layout optimization through a co-design approach. This study estimates the potential of this method for a wide range of realistic conditions, adopting a tailored genetic algorithm and novel geometric yaw relations. A gain in the annual energy yield between 0.3 % and 0.4 % is obtained for a 16-tubrines farm and a multi-objective implementation is used to limit the loss in case wake steering is not used during the farm operation.
Maarten J. van den Broek, Marcus Becker, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, https://doi.org/10.5194/wes-9-721-2024, 2024
Short summary
Short summary
Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
Livia Brandetti, Sebastiaan Paul Mulders, Roberto Merino-Martinez, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 471–493, https://doi.org/10.5194/wes-9-471-2024, https://doi.org/10.5194/wes-9-471-2024, 2024
Short summary
Short summary
This research presents a multi-objective optimisation approach to balance vertical-axis wind turbine (VAWT) performance and noise, comparing the combined wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller with a baseline. Psychoacoustic annoyance is used as a novel metric for human perception of wind turbine noise. Results showcase the WSE–TSR tracking controller’s potential in trading off the considered objectives, thereby fostering the deployment of VAWTs in urban areas.
Shyam VimalKumar, Delphine De Tavernier, Dominic von Terzi, Marco Belloli, and Axelle Viré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-10, https://doi.org/10.5194/wes-2024-10, 2024
Preprint under review for WES
Short summary
Short summary
When standing still without a nacelle or blades, the vibrations on the wind turbine tower are a concern to its structural health. This study finds that the air which flows around the tower recirculates behind the tower, forming so-called wakes. This wakes initiates the vibration, and the movement itself keeps the vibration increasing or decreasing depending on the wind speed. The current study uses a methodology called Force-partitioning to analyse this in depth.
Amr Hegazy, Peter Naaijen, Vincent Leroy, Félicien Bonnefoy, Yves Pérignon, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-180, https://doi.org/10.5194/wes-2023-180, 2024
Preprint under review for WES
Short summary
Short summary
Successful wave tank experiments were conducted to evaluate the feedforward control strategy benefits in terms of the structural loads and the power quality of the floating wind turbine components. In short, the wave feedforward control strategy is effective when it comes to alleviating the effects of the wave forces on the FOWT, whereas, wave FF control requires significant amount of actuation to minimise the platform pitch motion, which makes such a technology unfavourable for that objective.
Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn
Wind Energ. Sci., 8, 1755–1770, https://doi.org/10.5194/wes-8-1755-2023, https://doi.org/10.5194/wes-8-1755-2023, 2023
Short summary
Short summary
This study examines wind vane measurements of commercial wind turbines and their impact on yaw control. The authors discovered that rotor interference can cause an overestimation of wind vane measurements, leading to overcorrection of the yaw controller. A correction function that improves the yaw behaviour is presented and validated in free-field experiments on a commercial wind turbine. This work provides new insights into wind direction measurements and suggests ways to optimize yaw control.
Livia Brandetti, Sebastiaan Paul Mulders, Yichao Liu, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1553–1573, https://doi.org/10.5194/wes-8-1553-2023, https://doi.org/10.5194/wes-8-1553-2023, 2023
Short summary
Short summary
This research presents the additional benefits of applying an advanced combined wind speed estimator and tip-speed ratio tracking (WSE–TSR) controller compared to the baseline Kω2. Using a frequency-domain framework and an optimal calibration procedure, the WSE–TSR tracking control scheme shows a more flexible trade-off between conflicting objectives: power maximisation and load minimisation. Therefore, implementing this controller on large-scale wind turbines will facilitate their operation.
Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn van Dooren, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-114, https://doi.org/10.5194/wes-2023-114, 2023
Revised manuscript under review for WES
Short summary
Short summary
The paper presents induction zone measurements conducted with two synchronised lidars at a two-turbine windfarm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake such as evidence of induction asymmetry and induction zone-wake interactions. The measurements will aid the process to further improve existing inflow and wake models.
Daniel van den Berg, Delphine de Tavernier, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 849–864, https://doi.org/10.5194/wes-8-849-2023, https://doi.org/10.5194/wes-8-849-2023, 2023
Short summary
Short summary
Wind turbines placed in farms interact with their wake, lowering the power production of the wind farm. This can be mitigated using so-called wake mixing techniques. This work investigates the coupling between the pulse wake mixing technique and the motion of floating wind turbines using the pulse. Frequency response experiments and time domain simulations show that extra movement is undesired and that the
optimalexcitation frequency is heavily platform dependent.
Balthazar Arnoldus Maria Sengers, Gerald Steinfeld, Paul Hulsman, and Martin Kühn
Wind Energ. Sci., 8, 747–770, https://doi.org/10.5194/wes-8-747-2023, https://doi.org/10.5194/wes-8-747-2023, 2023
Short summary
Short summary
The optimal misalignment angles for wake steering are determined using wake models. Although mostly analytical, data-driven models have recently shown promising results. This study validates a previously proposed data-driven model with results from a field experiment using lidar measurements. In a comparison with a state-of-the-art analytical model, it shows systematically more accurate estimates of the available power. Also when using only commonly available input data, it gives good results.
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).
Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, https://doi.org/10.5194/wes-7-2163-2022, 2022
Short summary
Short summary
In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
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.
Daan van der Hoek, Joeri Frederik, Ming Huang, Fulvio Scarano, Carlos Simao Ferreira, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 1305–1320, https://doi.org/10.5194/wes-7-1305-2022, https://doi.org/10.5194/wes-7-1305-2022, 2022
Short summary
Short summary
The paper presents a wind tunnel experiment where dynamic induction control was implemented on a small-scale turbine. By periodically changing the pitch angle of the blades, the low-velocity turbine wake is perturbed, and hence it recovers at a faster rate. Small particles were released in the flow and subsequently recorded with a set of high-speed cameras. This allowed us to reconstruct the flow behind the turbine and investigate the effect of dynamic induction control on the wake.
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022, https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Short summary
An accurate prediction of loads and power of an offshore wind turbine is needed for an optimal design. However, such predictions are typically performed with engineering models that contain many inaccuracies and uncertainties. In this paper we have proposed a systematic approach to quantify and calibrate these uncertainties based on two experimental datasets. The calibrated models are much closer to the experimental data and are equipped with an estimate of the uncertainty in the predictions.
Yichao Liu, Riccardo Ferrari, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 523–537, https://doi.org/10.5194/wes-7-523-2022, https://doi.org/10.5194/wes-7-523-2022, 2022
Short summary
Short summary
The objective of the paper is to develop a data-driven output-constrained individual pitch control approach, which will not only mitigate the blade loads but also reduce the pitch activities. This is achieved by only reducing the blade loads violating a user-defined bound, which leads to an economically viable load control strategy. The proposed control strategy shows promising results of load reduction in the wake-rotor overlapping and turbulent sheared wind conditions.
Unai Gutierrez Santiago, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 505–521, https://doi.org/10.5194/wes-7-505-2022, https://doi.org/10.5194/wes-7-505-2022, 2022
Short summary
Short summary
The gearbox is one of the main contributors to the overall cost of wind energy, and it is acknowledged that we still do not fully understand its loading. The study presented in this paper develops a new alternative method to measure input rotor torque in wind turbine gearboxes, overcoming the drawbacks related to measuring on a rotating shaft. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines.
Aemilius A. W. van Vondelen, Sachin T. Navalkar, Alexandros Iliopoulos, Daan C. van der Hoek, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 161–184, https://doi.org/10.5194/wes-7-161-2022, https://doi.org/10.5194/wes-7-161-2022, 2022
Short summary
Short summary
The damping of an offshore wind turbine is a difficult physical quantity to predict, although it plays a major role in a cost-effective turbine design. This paper presents a review of all approaches that can be used for damping estimation directly from operational wind turbine data. As each use case is different, a novel suitability table is presented to enable the user to choose the most appropriate approach for the given availability and characteristics of measurement data.
Alessandro Fontanella, Mees Al, Jan-Willem van Wingerden, and Marco Belloli
Wind Energ. Sci., 6, 885–901, https://doi.org/10.5194/wes-6-885-2021, https://doi.org/10.5194/wes-6-885-2021, 2021
Short summary
Short summary
Floating wind is a key technology to harvest the abundant wind energy resource of deep waters. This research introduces a new way of controlling the wind turbine to better deal with the action of waves. The turbine is made aware of the incoming waves, and the information is exploited to enhance power production.
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
Short summary
Short summary
This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, https://doi.org/10.5194/wes-5-309-2020, 2020
Short summary
Short summary
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
Joeri Alexis Frederik, Robin Weber, Stefano Cacciola, Filippo Campagnolo, Alessandro Croce, Carlo Bottasso, and Jan-Willem van Wingerden
Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, https://doi.org/10.5194/wes-5-245-2020, 2020
Short summary
Short summary
The interaction between wind turbines in a wind farm through their wakes is a widely studied research area. Until recently, research was focused on finding constant turbine inputs that optimize the performance of the wind farm. However, recent studies have shown that time-varying, dynamic inputs might be more beneficial. In this paper, the validity of this approach is further investigated by implementing it in scaled wind tunnel experiments and assessing load effects, showing promising results.
Steffen Raach, Bart Doekemeijer, Sjoerd Boersma, Jan-Willem van Wingerden, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-54, https://doi.org/10.5194/wes-2019-54, 2019
Publication in WES not foreseen
Short summary
Short summary
The presented work combines two control approaches of wake redirection control, feedforward wake redirection and feedback wake redirction. In our previous investigatins the lidar-assisted feedback control was studied and the advantages and disadvantages were discussed. The optimal yaw angles for the wind turbines are precomputed, the feedback takes care of uncertainties and disturbances. The concept is demonstrated in a high fidelity simulation model.
Hector Mendez Reyes, Stoyan Kanev, Bart Doekemeijer, and Jan-Willem van Wingerden
Wind Energ. Sci., 4, 549–561, https://doi.org/10.5194/wes-4-549-2019, https://doi.org/10.5194/wes-4-549-2019, 2019
Short summary
Short summary
Within wind farms, the wind turbines interact with each other through their wakes. Turbines operating in these wakes have lower power production and increased wear and tear. Wake redirection is control strategy to steer the wakes aside from downstream turbines, increasing the power yield of the farm. Models for predicting the power gain and impacts on wear exist, but they are still immature and require validation. The validation of such a model is the purpose of this paper.
Andreas Rott, Bart Doekemeijer, Janna Kristina Seifert, Jan-Willem van Wingerden, and Martin Kühn
Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, https://doi.org/10.5194/wes-3-869-2018, 2018
Short summary
Short summary
Active wake deflection (AWD) aims to increase the power output of a wind farm by misaligning the yaw of upstream turbines. We analysed the effect of dynamic wind direction changes on AWD. The results show that AWD is very sensitive towards these dynamics. Therefore, we present a robust active wake control, which considers uncertainties and wind direction changes, increasing the overall power output of a wind farm. A side effect is a significant reduction of the yaw actuation of the turbines.
Bart M. Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 749–765, https://doi.org/10.5194/wes-3-749-2018, https://doi.org/10.5194/wes-3-749-2018, 2018
Short summary
Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
Sebastiaan Paul Mulders, Niels Frederik Boudewijn Diepeveen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 615–638, https://doi.org/10.5194/wes-3-615-2018, https://doi.org/10.5194/wes-3-615-2018, 2018
Short summary
Short summary
The modeling, operating strategy, and controller design for an actual in-field wind turbine with a fixed-displacement hydraulic drivetrain are presented. An analysis is given on a passive torque control strategy for below-rated operation. The turbine lacks the option to influence the system torque by a generator, so the turbine is regulated by a spear valve in the region between below- and above-rated operation. The control design is evaluated on a real-world 500 kW hydraulic wind turbine.
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, https://doi.org/10.5194/wes-3-75-2018, 2018
Short summary
Short summary
Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Edwin van Solingen, Sebastiaan Paul Mulders, and Jan-Willem van Wingerden
Wind Energ. Sci., 2, 153–173, https://doi.org/10.5194/wes-2-153-2017, https://doi.org/10.5194/wes-2-153-2017, 2017
Short summary
Short summary
The aim of this paper is to show that with an automated tuning strategy, wind turbine control performance can be significantly increased. To this end, iterative feedback tuning (IFT) is applied to two different turbine controllers. The results obtained by high-fidelity simulations indicate significant performance improvements over baseline controllers. It is concluded that IFT of turbine controllers has the potential to become a valuable tool for improving wind turbine performance.
Sachin T. Navalkar, Lars O. Bernhammer, Jurij Sodja, Edwin van Solingen, Gijs A. M. van Kuik, and Jan-Willem van Wingerden
Wind Energ. Sci., 1, 205–220, https://doi.org/10.5194/wes-1-205-2016, https://doi.org/10.5194/wes-1-205-2016, 2016
Short summary
Short summary
In order to reduce the cost of wind energy, it is necessary to reduce the loads that wind turbines withstand over their lifetime. The combination of blade rotation with newly designed blade shape changing actuators is demonstrated experimentally. While load reduction is achieved, the additional flexibility implies that careful control design is needed to avoid instability.
G. A. M. van Kuik, J. Peinke, R. Nijssen, D. Lekou, J. Mann, J. N. Sørensen, C. Ferreira, J. W. van Wingerden, D. Schlipf, P. Gebraad, H. Polinder, A. Abrahamsen, G. J. W. van Bussel, J. D. Sørensen, P. Tavner, C. L. Bottasso, M. Muskulus, D. Matha, H. J. Lindeboom, S. Degraer, O. Kramer, S. Lehnhoff, M. Sonnenschein, P. E. Sørensen, R. W. Künneke, P. E. Morthorst, and K. Skytte
Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, https://doi.org/10.5194/wes-1-1-2016, 2016
Related subject area
Thematic area: Fluid mechanics | Topic: Wakes and wind farm aerodynamics
The near-wake development of a wind turbine operating in stalled conditions – Part 1: Assessment of numerical models
Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Floating wind turbine motion signature in the far-wake spectral content – a wind tunnel experiment
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling
A method to correct for the effect of blockage and wakes on power performance measurements
Improvements to the Dynamic Wake Meandering Model by incorporating the turbulent Schmidt number
Wind Farm Structural Response and Wake Dynamics for an Evolving Stable Boundary Layer: Computational and Experimental Comparisons
Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization
Synchronised WindScanner Field Measurements of the Induction Zone Between Two Closely Spaced Wind Turbines
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm applied on FLORIS
Vortex model of the aerodynamic wake of airborne wind energy systems
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling
Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
An Actuator Sector Model for Wind Power Applications: A Parametric Study
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model
Actuator line model using simplified force calculation methods
Brief communication: A clarification of wake recovery mechanisms
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics
Wind turbine wake simulation with explicit algebraic Reynolds stress modeling
Including realistic upper atmospheres in a wind-farm gravity-wave model
Pascal Weihing, Marion Cormier, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 9, 933–962, https://doi.org/10.5194/wes-9-933-2024, https://doi.org/10.5194/wes-9-933-2024, 2024
Short summary
Short summary
This study evaluates different approaches to simulate the near-wake flow of a wind turbine. The test case is in off-design conditions of the wind turbine, where the flow is separated from the blades and therefore very difficult to predict. The evaluation of simulation techniques is key to understand their limitations and to deepen the understanding of the near-wake physics. This knowledge can help to derive new wind farm design methods for yield-optimized farm layouts.
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri
Wind Energ. Sci., 9, 869–882, https://doi.org/10.5194/wes-9-869-2024, https://doi.org/10.5194/wes-9-869-2024, 2024
Short summary
Short summary
This paper proposes a computational method to maximise the power production of wind farms through two strategies: layout optimisation and yaw angle optimisation. The proposed method relies on high-fidelity computational modelling of wind farm flows and is shown to be able to effectively maximise wind farm power production. Performance improvements relative to conventional optimisation strategies based on low-fidelity models can be attained, particularly in scenarios of increased flow complexity.
Benyamin Schliffke, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 9, 519–532, https://doi.org/10.5194/wes-9-519-2024, https://doi.org/10.5194/wes-9-519-2024, 2024
Short summary
Short summary
This paper studies the consequences of floater motions for the wake properties of a floating wind turbine. Since wake interactions are responsible for power production loss in wind farms, it is important that we know whether the tools that are used to predict this production loss need to be upgraded to take into account these aspects. Our wind tunnel study shows that the signature of harmonic floating motions can be observed in the far wake of a wind turbine, when motions have strong amplitudes.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 97–117, https://doi.org/10.5194/wes-9-97-2024, https://doi.org/10.5194/wes-9-97-2024, 2024
Short summary
Short summary
Wind turbine wakes affect the production and lifecycle of downstream turbines. They can be predicted with the dynamic wake meandering (DWM) method. In this paper, the authors break down the velocity and turbulence in the wake of a wind turbine into several terms. They show that it is implicitly assumed in the DWM that some of these terms are neglected. With high-fidelity simulations, it is shown that this can lead to some errors, in particular for the maximum turbulence added by the wake.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 119–139, https://doi.org/10.5194/wes-9-119-2024, https://doi.org/10.5194/wes-9-119-2024, 2024
Short summary
Short summary
Analytical models allow us to quickly compute the decreased power output and lifetime induced by wakes in a wind farm. This is achieved by evaluating the modified velocity and turbulence in the wake. In this work, we present a new model based on the velocity and turbulence breakdowns presented in Part 1. This new model is physically based, allows us to compute the whole turbulence profile (rather than the maximum value) and is built to take atmospheric stability into account.
Alessandro Sebastiani, James Bleeg, and Alfredo Peña
Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
Short summary
Short summary
The power curve of a wind turbine indicates the turbine power output in relation to the wind speed. Therefore, power curves are critically important to estimate the production of future wind farms as well as to assess whether operating wind farms are functioning correctly. Since power curves are often measured in wind farms, they might be affected by the interactions between the turbines. We show that these effects are not negligible and present a method to correct for them.
Peter Brugger, Corey Markfort, and Fernando Porté-Agel
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-150, https://doi.org/10.5194/wes-2023-150, 2023
Revised manuscript accepted for WES
Short summary
Short summary
The dynamic wake meandering model (DWMM) assumes that wind turbine wakes are transported like a passive tracer by the large-scale turbulence of the atmospheric boundary layer. We show that both the streamwise transport and the lateral transport have differences to the passive tracer assumption. We then propose to include the turbulent Schmidt number into the DWMM to address the observed differences and show that it improves the quality of the model predictions.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-138, https://doi.org/10.5194/wes-2023-138, 2023
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Franz Volker Mühle, Florian Marco Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-125, https://doi.org/10.5194/wes-2023-125, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Wind turbines influence each other and this wake effects limit the power production of downstream turbines. Controlling turbines collectively and not individually can limit such effects. We experimentally investigate a control strategy increasing mixing in the wake. We want to see the potential of this so called Helix control for power optimization and understand the flow physics. Our study shows that the control technique leads to clearly faster wake recovery and thus higher power production.
Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn van Dooren, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-114, https://doi.org/10.5194/wes-2023-114, 2023
Revised manuscript under review for WES
Short summary
Short summary
The paper presents induction zone measurements conducted with two synchronised lidars at a two-turbine windfarm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake such as evidence of induction asymmetry and induction zone-wake interactions. The measurements will aid the process to further improve existing inflow and wake models.
Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-98, https://doi.org/10.5194/wes-2023-98, 2023
Revised manuscript under review for WES
Short summary
Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging, due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Filippo Trevisi, Carlo E. D. Riboldi, and Alessandro Croce
Wind Energ. Sci., 8, 999–1016, https://doi.org/10.5194/wes-8-999-2023, https://doi.org/10.5194/wes-8-999-2023, 2023
Short summary
Short summary
Modeling the aerodynamic wake of airborne wind energy systems (AWESs) is crucial to properly estimating power production and to designing such systems. The velocities induced at the AWES from its own wake are studied with a model for the near wake and one for the far wake, using vortex methods. The model is validated with the lifting-line free-vortex wake method implemented in QBlade.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
Short summary
Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
Peter Baas, Remco Verzijlbergh, Pim van Dorp, and Harm Jonker
Wind Energ. Sci., 8, 787–805, https://doi.org/10.5194/wes-8-787-2023, https://doi.org/10.5194/wes-8-787-2023, 2023
Short summary
Short summary
This work studies the energy production and wake losses of large offshore wind farms with a large-eddy simulation model. Therefore, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
Short summary
Short summary
The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-16, https://doi.org/10.5194/wes-2023-16, 2023
Revised manuscript accepted for WES
Short summary
Short summary
This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model which is one of the most common ways used for numerical simulations of wind farms while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Tamino Wetz and Norman Wildmann
Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023, https://doi.org/10.5194/wes-8-515-2023, 2023
Short summary
Short summary
In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field measurements on an operating 2 MW wind turbine (WT) in complex terrain. The fleet of multirotors has the potential to fill the meteorological gap of observations in the near wake of WTs with high-temporal and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. The flow up- and downstream of the WT is measured simultaneously at multiple spatial positions.
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.
Gonzalo Pablo Navarro Diaz, Alejandro Daniel Otero, Henrik Asmuth, Jens Nørkær Sørensen, and Stefan Ivanell
Wind Energ. Sci., 8, 363–382, https://doi.org/10.5194/wes-8-363-2023, https://doi.org/10.5194/wes-8-363-2023, 2023
Short summary
Short summary
In this paper, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.
Maarten Paul van der Laan, Mads Baungaard, and Mark Kelly
Wind Energ. Sci., 8, 247–254, https://doi.org/10.5194/wes-8-247-2023, https://doi.org/10.5194/wes-8-247-2023, 2023
Short summary
Short summary
Understanding wind turbine wake recovery is important to mitigate energy losses in wind farms. Wake recovery is often assumed or explained to be dependent on the first-order derivative of velocity. In this work we show that wind turbine wakes recover mainly due to the second-order derivative of the velocity, which transport momentum from the freestream towards the wake center. The wake recovery mechanisms and results of a high-fidelity numerical simulation are illustrated using a simple model.
Søren Juhl Andersen and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 2117–2133, https://doi.org/10.5194/wes-7-2117-2022, https://doi.org/10.5194/wes-7-2117-2022, 2022
Short summary
Short summary
Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 1975–2002, https://doi.org/10.5194/wes-7-1975-2022, https://doi.org/10.5194/wes-7-1975-2022, 2022
Short summary
Short summary
Wind turbine wakes in the neutral atmospheric surface layer are simulated with Reynolds-averaged Navier–Stokes (RANS) using an explicit algebraic Reynolds stress model. Contrary to standard two-equation turbulence models, it can predict turbulence anisotropy and complex physical phenomena like secondary motions. For the cases considered, it improves Reynolds stress, turbulence intensity, and velocity deficit predictions, although a more top-hat-shaped profile is observed for the latter.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
Short summary
Short summary
Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Cited articles
Bartl, J., Mühle, F., Schottler, J., Sætran, L., Peinke, J., Adaramola, M., and Hölling, M.: Wind tunnel experiments on wind turbine wakes in yaw: effects of inflow turbulence and shear, Wind Energ. Sci., 3, 329–343, https://doi.org/10.5194/wes-3-329-2018, 2018. a
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a
Becker, M., Ritter, B., Doekemeijer, B., van der Hoek, D., Konigorski, U., Allaerts, D., and van Wingerden, J.-W.: The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake, Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, 2022. a
Bhagwat, M. J. and Leishman, J. G.: Transient rotor inflow using a time-accurate free-vortex wake model, in: 39th Aerosp. Sci. Meet. Exhib., p. 993, https://doi.org/10.2514/6.2001-993, 2001. a
Boersma, S., Doekemeijer, B., Vali, M., Meyers, J., and van Wingerden, J.-W.: A control-oriented dynamic wind farm model: WFSim, Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, 2018. a
Branlard, E., Martínez-Tossas, L. A., and Jonkman, J.: A time-varying formulation of the curled wake model within the FAST.Farm framework, Wind Energy, 26, 44–63, https://doi.org/10.1002/we.2785, 2023. a
Brown, K., Houck, D., Maniaci, D., and Westergaard, C.: Rapidly recovering wind turbine wakes with dynamic pitch and rotor speed control, in: AIAA Scitech 2021 Forum, p. 1182, ISBN 9781624106095, https://doi.org/10.2514/6.2021-1182, 2021. a
Burton, T., Sharpe, D., Jenkins, N., and Bossanyi, E.: Wind Energy Handbook, Wiley, ISBN 0471489972, 2001. a
de Vaal, J. B., Hansen, M. O., and Moan, T.: Validation of a vortex ring wake model suited for aeroelastic simulations of floating wind turbines, J. Phys. Conf. Ser., 555, 012025, https://doi.org/10.1088/1742-6596/555/1/012025, 2014. 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
Dong, J., Viré, A., Ferreira, C. S., Li, Z., and van Bussel, G.: A modified free wake vortex ring method for horizontal-axis wind turbines, Energies, 12, 3900, https://doi.org/10.3390/en12203900, 2019. a
Fleming, P., Annoni, J., Martínez-Tossas, L. A., Raach, S., Gruchalla, K., Scholbrock, A., Churchfield, M., and Roadman, J.: Investigation into the shape of a wake of a yawed full-scale turbine, J. Phys. Conf. Ser., 1037, 032010, https://doi.org/10.1088/1742-6596/1037/3/032010, 2018. a
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
Fleming, P., Sinner, M., Young, T., Lannic, M., King, J., Simley, E., and Doekemeijer, B.: Experimental results of wake steering using fixed angles, Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, 2021. a
Houck, D. R., DeVelder, N., and Kelley, C. L.: Comparison of a mid-fidelity free vortex wake method to a high-fidelity actuator line model large eddy simulation for wind turbine wake simulations, J. Phys. Conf. Ser., 2265, 042044, https://doi.org/10.1088/1742-6596/2265/4/042044, 2022. a
Howland, M. F., Bossuyt, J., Martínez-Tossas, L. A., Meyers, J., and Meneveau, C.: Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions, J. Renew. Sustain. Ener., 8, 043301, https://doi.org/10.1063/1.4955091, 2016. a
Howland, M. F., Lele, S. K., and Dabiri, J. O.: Wind farm power optimization through wake steering, P. Natl. Acad. Sci. USA, 116, 14495–14500, https://doi.org/10.1073/pnas.1903680116, 2019. a
Hulsman, P., Wosnik, M., Petrović, V., Hölling, M., and Kühn, M.: Data Supplement for “Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow” – Wind Energy Science Journal, Zenodo [data set], https://doi.org/10.5281/zenodo.5734877, 2021. a
Hulsman, P., Sucameli, C., Petrović, V., Rott, A., Gerds, A., and Kühn, M.: Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions, J. Phys. Conf. Ser., 2265, 032074, https://doi.org/10.1088/1742-6596/2265/3/032074, 2022a. a
Hulsman, P., Wosnik, M., Petrović, V., Hölling, M., and Kühn, M.: Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow, Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, 2022b. a, b, c, d
Janssens, N. and Meyers, J.: Towards real-time optimal control of wind farms using large-eddysimulations, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-84, in review, 2023. a
Jeon, M., Lee, S., Kim, T., and Lee, S.: Wake influence on dynamic load characteristics of offshore floating wind turbines, AIAA J., 54, 3535–3545, https://doi.org/10.2514/1.J054584, 2016. a
Lee, H. and Lee, D. J.: Effects of platform motions on aerodynamic performance and unsteady wake evolution of a floating offshore wind turbine, Renew. Energ., 143, 9–23, https://doi.org/10.1016/j.renene.2019.04.134, 2019. a
Leishman, J. G.: Challenges in modeling the unsteady aerodynamics of wind turbines, Wind Energy, 5, 85–132, https://doi.org/10.1002/we.62, 2002. a
Lejeune, M., Moens, M., and Chatelain, P.: A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction, Front. Energy Res., 10, 884068, https://doi.org/10.3389/fenrg.2022.884068, 2022. a
Lin, M. and Porté-Agel, F.: Large-eddy simulation of a wind-turbine array subjected to active yaw control, Wind Energ. Sci., 7, 2215–2230, https://doi.org/10.5194/wes-7-2215-2022, 2022. 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, b
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a
Munters, W. and Meyers, J.: Optimal dynamic induction and yaw control of wind farms: Effects of turbine spacing and layout, J. Phys. Conf. Ser., 1037, 032015, https://doi.org/10.1088/1742-6596/1037/3/032015, 2018. a
NREL: FLORIS. Version 3.0, https://github.com/NREL/floris (last access: 4 November 2023), 2022. a
Petrović, V., Schottler, J., Neunaber, I., Hölling, M., and Kühn, M.: Wind tunnel validation of a closed loop active power control for wind farms, J. Phys. Conf. Ser., 1037, 032020, https://doi.org/10.1088/1742-6596/1037/3/032020, 2018. a
Schottler, J., Hölling, A., Peinke, J., and Hölling, M.: Wind tunnel tests on controllable model wind turbines in yaw, in: 34th Wind Energy Symp., AIAA, ISBN 9781624103957, https://doi.org/10.2514/6.2016-1523, 2016. a
Schottler, J., Bartl, J., Mühle, F., Sætran, L., Peinke, J., and Hölling, M.: Wind tunnel experiments on wind turbine wakes in yaw: redefining the wake width, Wind Energ. Sci., 3, 257–273, https://doi.org/10.5194/wes-3-257-2018, 2018. a
Sebastian, T. and Lackner, M.: Analysis of the Induction and Wake Evolution of an Offshore Floating Wind Turbine, Energies, 5, 968–1000, https://doi.org/10.3390/en5040968, 2012. a
Simley, E., Fleming, P., Girard, N., Alloin, L., Godefroy, E., and Duc, T.: Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, 2021. a
Simoes, F. J. and Graham, J. M.: Application of a free vortex wake model to a horizontal axis wind turbine, J. Wind Eng. Ind. Aerod., 39, 129–138, https://doi.org/10.1016/0167-6105(92)90539-M, 1992. a
Squire, H. B.: The Growth of a Vortex in Turbulent Flow, Aeronaut. Quart., 16, 302–306, https://doi.org/10.1017/s0001925900003516, 1965. a
van den Broek, M. J.: Simulation data and code underlying the publication: Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects, 4TU.ResearchData [data set and code], https://doi.org/10.4121/e32a9868-c5ea-43d3-8969-b1908662b2b2, 2023. a
van den Broek, M. J. and van Wingerden, J. W.: Dynamic Flow Modelling for Model-Predictive Wind Farm Control, J. Phys. Conf. Ser., 1618, 022023, https://doi.org/10.1088/1742-6596/1618/2/022023, 2020. a
van den Broek, M. J., De Tavernier, D., Sanderse, B., and van Wingerden, J. W.: Adjoint optimisation for wind farm flow control with a free-vortex wake model, Renew. Energ., 201, 752–765, https://doi.org/10.1016/j.renene.2022.10.120, 2022a. a, b, c
van den Broek, M. J., Sanderse, B., and van Wingerden, J. W.: Flow Modelling for Wind Farm Control: 2D vs. 3D, J. Phys. Conf. Ser., 2265, 032086, https://doi.org/10.1088/1742-6596/2265/3/032086, 2022b. a
van den Broek, M. J., van den Berg, D., Sanderse, B., and van Wingerden, J. W.: Optimal Control for Wind Turbine Wake Mixing on Floating Platforms, IFAC-PapersOnLine, 56, 7656–7661, https://doi.org/10.1016/j.ifacol.2023.10.1165, 2023. a
van der Hoek, D., van den Broek, M. J., and van Wingerden, J. W.: Data underlying the publication: Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects, 4TU.ResearchData [data set], https://doi.org/10.4121/70ae7f4c-f87f-45f1-8360-f4958a60249f.v1, 2023b. a
van Kuik, G. A. M.: The Fluid Dynamic Basis for Actuator Disc and Rotor Theories, ISBN 9781614998662, https://doi.org/10.3233/978-1-61499-866-2-i, 2018. a, b, c
van Wingerden, J. W., Fleming, P. A., Göcmen, T., Eguinoa, I., Doekemeijer, B. M., Dykes, K., Lawson, M., Simley, E., King, J., Astrain, D., Iribas, M., Bottasso, C. L., Meyers, J., Raach, S., Kölle, K., and Giebel, G.: Expert Elicitation on Wind Farm Control, J. Phys. Conf. Ser., 1618, 022025, https://doi.org/10.1088/1742-6596/1618/2/022025, 2020. a
Short summary
As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three...
Altmetrics
Final-revised paper
Preprint