Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-1791-2022
© Author(s) 2022. 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-7-1791-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2022
Research article |
| 08 Sep 2022
| 08 Sep 2022
FarmConners wind farm flow control benchmark – Part 1: Blind test results
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Filippo Campagnolo
Wind Energy Institute, Technische Universität München, 85748 Garching b. München, Germany
Thomas Duc
ENGIE Green, 6 rue Alexander Fleming, 69007 Lyon, France
Irene Eguinoa
Wind Energy Department, CENER, Sarriguren, Spain
Søren Juhl Andersen
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Vlaho Petrović
ForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
Lejla Imširović
Wind Energy Institute, Technische Universität München, 85748 Garching b. München, Germany
Robert Braunbehrens
Wind Energy Institute, Technische Universität München, 85748 Garching b. München, Germany
Jaime Liew
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Mads Baungaard
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Maarten Paul van der Laan
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Guowei Qian
Department of Civil Engineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan
Maria Aparicio-Sanchez
Wind Energy Department, CENER, Sarriguren, Spain
Rubén González-Lope
Wind Energy Department, CENER, Sarriguren, Spain
Vinit V. Dighe
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Marcus Becker
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Maarten J. van den Broek
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Adam Stock
Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, The University of Strathclyde, Glasgow, UK
Matthew Cole
Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, The University of Strathclyde, Glasgow, UK
Renzo Ruisi
DNV, Group Research & Development, Bristol, United Kingdom
Ervin Bossanyi
DNV, Group Research & Development, Bristol, United Kingdom
Niklas Requate
Fraunhofer IWES, Bremerhaven, Germany
Simon Strnad
Fraunhofer IWES, Bremerhaven, Germany
Jonas Schmidt
Fraunhofer IWES, Bremerhaven, Germany
Lukas Vollmer
Fraunhofer IWES, Bremerhaven, Germany
Ishaan Sood
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, Leuven 3001, Belgium
Johan Meyers
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, Leuven 3001, Belgium
Related authors
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci., 10, 559–578, https://doi.org/10.5194/wes-10-559-2025, https://doi.org/10.5194/wes-10-559-2025, 2025
Short summary
Short summary
This research develops a new method for assessing hybrid power plant (HPP) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art energy management system (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 1811–1826, https://doi.org/10.5194/wes-9-1811-2024, https://doi.org/10.5194/wes-9-1811-2024, 2024
Short summary
Short summary
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Jaime Liew, Tuhfe Göçmen, Alan W. H. Lio, and Gunner Chr. Larsen
Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, https://doi.org/10.5194/wes-8-1387-2023, 2023
Short summary
Short summary
We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
Short summary
Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
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).
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio
Wind Energ. Sci., 6, 111–129, https://doi.org/10.5194/wes-6-111-2021, https://doi.org/10.5194/wes-6-111-2021, 2021
Short summary
Short summary
Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
Kirby S. Heck, Jaime Liew, Ilan M. L. Upfal, and Michael F. Howland
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-90, https://doi.org/10.5194/wes-2025-90, 2025
Preprint under review for WES
Short summary
Short summary
Wind turbines within a wind farm can be controlled collectively, rather than individually, to mitigate wake interactions between turbines and enhance power production. Here, we join two common wind farm control strategies synergistically, enabled by advances in aerodynamic rotor modeling. The joint strategy outperforms other control strategies when compared with advanced computer simulations, and future improvements on wake modeling can further extend these power gains for large wind farms.
Marcus Becker, Maxime Lejeune, Philippe Chatelain, Dries Allaerts, Rafael Mudafort, and Jan-Willem van Wingerden
Wind Energ. Sci., 10, 1055–1075, https://doi.org/10.5194/wes-10-1055-2025, https://doi.org/10.5194/wes-10-1055-2025, 2025
Short summary
Short summary
Established turbine wake models are steady-state. This paper presents an open-source dynamic wake modeling framework that complements established steady-state wake models with dynamics. It is advantageous over steady-state wake models to describe wind farm power and energy over shorter periods. The model enables researchers to investigate the effectiveness of wind farm flow control strategies. This leads to a better utilization of wind farms and allows them to be used to their fullest extent.
Stefan Ivanell, Warit Chanprasert, Luca Lanzilao, James Bleeg, Johan Meyers, Antoine Mathieu, Søren Juhl Andersen, Rem-Sophia Mouradi, Eric Dupont, Hugo Olivares-Espinosa, and Niels Troldborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-88, https://doi.org/10.5194/wes-2025-88, 2025
Preprint under review for WES
Short summary
Short summary
This study explores how the height of the atmosphere's boundary layer impacts wind farm performance, focusing on how this factor influences energy output. By simulating different boundary layer heights and conditions, the research reveals that deeper layers promote better energy recovery. The findings highlight the importance of considering atmospheric conditions when simulating wind farms to maximize energy efficiency, offering valuable insights for the wind energy industry.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-66, https://doi.org/10.5194/wes-2025-66, 2025
Preprint under review for WES
Short summary
Short summary
We proposed a new method for active power control that uniquely combines induction control with wake steering to maximize power tracking margins. Our methodology results in significantly improved robustness against wind fluctuations and fatigue loading when compared to the state of the art.
Abhinav Anand, Robert Braunbehrens, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-67, https://doi.org/10.5194/wes-2025-67, 2025
Revised manuscript has not been submitted
Short summary
Short summary
We present a new method for wind farm control, based on the optimization of an economic cost function that accounts for revenue from power production and cost due to operation and maintenance. The new formulation also includes constraints to ensure a desired lifetime duration. The application to relevant scenarios shows consistently improved profit when compared to alternative formulations from the recent literature.
Amr Hegazy, Peter Naaijen, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-68, https://doi.org/10.5194/wes-2025-68, 2025
Preprint under review for WES
Short summary
Short summary
Floating wind turbines face stability issues when traditional onshore control methods are used, due to their motion at sea. This research reviews existing control strategies and introduces a new controller that improves stability without needing extra sensors. Simulations show it outperforms others in maintaining performance and reducing structural stress. The study highlights key trade-offs and the need for smarter, tailored control in offshore wind energy.
Aemilius Adrianus Wilhelmus van Vondelen, Marion Coquelet, Sachin Tejwant Navalkar, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-51, https://doi.org/10.5194/wes-2025-51, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Wind farms suffer energy losses due to wake effects between turbines. We present a new control strategy that synchronizes turbine wakes to enhance power output. By estimating and aligning the phase shifts of periodic wake structures using an advanced filtering method, downstream turbines recover more energy. Simulations show up to 10 % increased power at the third turbine. These results offer a promising path to improving wind farm efficiency while mixing wakes.
Mads Baungaard, Takafumi Nishino, and Maarten Paul van der Laan
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-50, https://doi.org/10.5194/wes-2025-50, 2025
Manuscript not accepted for further review
Short summary
Short summary
A systematic comparison between 2D and 3D Reynolds-averaged Navier-Stokes simulations of wind farm flows have been conducted. It is found that the 2D simulations are, at least, two orders of magnitude computationally cheaper than their corresponding 3D simulations, while the predicted farm power is within -30 % to 15 % for all cases considered. The large computational speed-ups and sensible results makes 2D simulations a promising option in the low- to mid-fidelity range.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Théo Delvaux and Johan Meyers
Wind Energ. Sci., 10, 613–630, https://doi.org/10.5194/wes-10-613-2025, https://doi.org/10.5194/wes-10-613-2025, 2025
Short summary
Short summary
The work explores the potential for wind farm load reduction and power maximization. We carried out a series of high-fidelity large-eddy simulations for a wide range of atmospheric conditions and operating regimes. Because of turbine-scale interactions and large-scale effects, we observed that maximum power extraction is achieved at regimes lower than the Betz operating point. Thus, we proposed three simple approaches with which thrust significantly decreases with only a limited impact on power.
Juan Felipe Céspedes Moreno, Juan Pablo Murcia León, and Søren Juhl Andersen
Wind Energ. Sci., 10, 597–611, https://doi.org/10.5194/wes-10-597-2025, https://doi.org/10.5194/wes-10-597-2025, 2025
Short summary
Short summary
Using a global base in a proper orthogonal decomposition provides a common base for analyzing flows, such as wind turbine wakes, across an entire parameter space. This can be used to compare flows with different conditions using the same physical interpretation. This work shows the convergence of the global base, its small error compared to the truncation error in the flow reconstruction, and the insensitivity to which datasets are included for generating the global base.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci., 10, 559–578, https://doi.org/10.5194/wes-10-559-2025, https://doi.org/10.5194/wes-10-559-2025, 2025
Short summary
Short summary
This research develops a new method for assessing hybrid power plant (HPP) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art energy management system (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
Andrew Kirby, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers
Wind Energ. Sci., 10, 435–450, https://doi.org/10.5194/wes-10-435-2025, https://doi.org/10.5194/wes-10-435-2025, 2025
Short summary
Short summary
Traditionally, the aerodynamic loss of wind farm efficiency is classified into wake loss and farm blockage loss. This study, using high-fidelity simulations, shows that neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called turbine-scale efficiency and farm-scale efficiency to better describe turbine–wake effects and farm–atmosphere interactions. This study suggests the importance of better modelling farm-scale loss in future studies.
Olivier Ndindayino, Augustin Puel, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-6, https://doi.org/10.5194/wes-2025-6, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Our aim is to understand the relationship between flow blockage and improved wind farm efficiency using large-eddy simulations, as well as developing an analytical model that shows promise for improving turbine power predictions under blockage. We found that blockage enhances turbine power and thrust by inducing a favourable pressure drop across the turbine row, while simultaneously inducing an unfavourable pressure increase downstream which has minimal direct impact on far wake development.
Unai Gutierrez Santiago, Aemilius A. W. van Vondelen, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 10, 207–225, https://doi.org/10.5194/wes-10-207-2025, https://doi.org/10.5194/wes-10-207-2025, 2025
Short summary
Short summary
Knowing the loads applied to wind turbine gearboxes throughout their service life is becoming increasingly important as maintaining reliability with higher torque density demands is proving to be challenging. Operational deflection shapes identified from fiber-optic strain measurements have enabled the estimation of input torque, improving the assessment of the consumed life. Tracking operational deflection shapes recursively over time can potentially be used as an indicator of fault detection.
Manuel Alejandro Zúñiga Inestroza, Paul Hulsman, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-171, https://doi.org/10.5194/wes-2024-171, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Wake effects cause power losses that degrade wind farm efficiency. This paper presents a wind tunnel investigation of dynamic induction control (DIC), a strategy to mitigate wake losses by improving turbine-flow interactions. WindScanner lidar measurements are used to explore the wake development of model turbines in response to DIC. Our results demonstrate consistent benefits and adaptability under realistic inflow conditions, highlighting DIC’s potential to increase wind farm power production.
Claudia Muscari, Paolo Schito, Axelle Viré, Alberto Zasso, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-149, https://doi.org/10.5194/wes-2024-149, 2025
Publication in WES not foreseen
Short summary
Short summary
This paper presents the findings of a study aimed at describing the flow system downstream of a wind turbine operated with a novel control technology. Results from heavy high-fidelity simulations are used to obtain a low-fidelity model that is quick enough to be used for the optimization of such technologies. Additionally, we were able to retrieve an improved understanding of the physics of such systems under different inflow conditions.
Sonja Steinbrück, Thorben Eilers, Lukas Vollmer, Kerstin Avila, and Gerald Steinfeld
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-146, https://doi.org/10.5194/wes-2024-146, 2024
Preprint withdrawn
Short summary
Short summary
This paper introduces an enhanced coupling between the LES code PALM and the aeroelastic code FAST, enabling detailed turbine output in temporally and spatially heterogeneous atmospheric flows while maintaining computational efficiency. A wind speed correction is added to reduce errors from force smearing on the numerical grid. Results were evaluated through comparisons between different model setups and turbine measurements, including assessments in a two-turbine wake situation.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Matteo Baricchio, Pieter M. O. Gebraad, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 2113–2132, https://doi.org/10.5194/wes-9-2113-2024, https://doi.org/10.5194/wes-9-2113-2024, 2024
Short summary
Short summary
Wake steering can be integrated into 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-tubrine farm, and a multi-objective implementation is used to limit loss in the case that wake steering is not used during farm operation.
Maarten Paul van der Laan, Mark Kelly, Mads Baungaard, Antariksh Dicholkar, and Emily Louise Hodgson
Wind Energ. Sci., 9, 1985–2000, https://doi.org/10.5194/wes-9-1985-2024, https://doi.org/10.5194/wes-9-1985-2024, 2024
Short summary
Short summary
Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a novel steady-state model of the atmospheric boundary layer (ABL) is introduced. Numerical wind turbine flow simulations subjected to shallow and tall ABLs are conducted, and the proposed model shows improved performance compared to other state-of-the-art steady-state models.
Marion Coquelet, Maxime Lejeune, Laurent Bricteux, Aemilius A. W. van Vondelen, Jan-Willem van Wingerden, and Philippe Chatelain
Wind Energ. Sci., 9, 1923–1940, https://doi.org/10.5194/wes-9-1923-2024, https://doi.org/10.5194/wes-9-1923-2024, 2024
Short summary
Short summary
An extended Kalman filter is used to estimate the wind impinging on a wind turbine based on the blade bending moments and a turbine model. Using large-eddy simulations, this paper verifies how robust the estimator is to the turbine control strategy as it impacts loads and operating parameters. It is shown that including dynamics in the turbine model to account for delays between actuation and bending moments is needed to maintain the accuracy of the estimator when dynamic pitch control is used.
Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 1811–1826, https://doi.org/10.5194/wes-9-1811-2024, https://doi.org/10.5194/wes-9-1811-2024, 2024
Short summary
Short summary
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Amr Hegazy, Peter Naaijen, Vincent Leroy, Félicien Bonnefoy, Mohammad Rasool Mojallizadeh, Yves Pérignon, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 1669–1688, https://doi.org/10.5194/wes-9-1669-2024, https://doi.org/10.5194/wes-9-1669-2024, 2024
Short summary
Short summary
Successful wave tank experiments were conducted to evaluate the feedforward (FF) control strategy benefits in terms of structural loads and power quality of floating wind turbine components. The wave FF control strategy is effective when it comes to alleviating the effects of the wave forces on the floating offshore wind turbines, whereas wave FF control requires a significant amount of actuation to minimize the platform pitch motion, which makes such technology unfavorable for that objective.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, https://doi.org/10.5194/wes-9-1547-2024, 2024
Short summary
Short summary
We develop a new simple model to predict power losses incurred by a wind turbine when it yaws out of the wind. The model reveals the effects of a number of rotor design parameters and how the turbine is governed when it yaws. The model exhibits an excellent agreement with large eddy simulations and wind tunnel measurements. We showcase the capabilities of the model by deriving the power-optimal yaw strategy for a single turbine and for a cluster of wake-interacting turbines.
Franz V. Mühle, Florian M. Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci., 9, 1251–1271, https://doi.org/10.5194/wes-9-1251-2024, https://doi.org/10.5194/wes-9-1251-2024, 2024
Short summary
Short summary
Wind turbines influence each other, and these 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.
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.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
Short summary
Short summary
Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
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.
Nick Janssens and Johan Meyers
Wind Energ. Sci., 9, 65–95, https://doi.org/10.5194/wes-9-65-2024, https://doi.org/10.5194/wes-9-65-2024, 2024
Short summary
Short summary
Proper wind farm control may vastly contribute to Europe's plan to go carbon neutral. However, current strategies don't account for turbine–wake interactions affecting power extraction. High-fidelity models (e.g., LES) are needed to accurately model this but are considered too slow in practice. By coarsening the resolution, we were able to design an efficient LES-based controller with real-time potential. This may allow us to bridge the gap towards practical wind farm control in the near future.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
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.
Niklas Requate, Tobias Meyer, and René Hofmann
Wind Energ. Sci., 8, 1727–1753, https://doi.org/10.5194/wes-8-1727-2023, https://doi.org/10.5194/wes-8-1727-2023, 2023
Short summary
Short summary
Wind turbines produce energy over a lifetime of at least 20 years, and they are designed to withstand the induced loads from the environment. During that long operating time, we cannot avoid causing damage to a turbine and using up the utilized materials. To gain maximum benefit from the material of each turbine, we developed a method which makes best use of their given design damage budget by optimally distributing its usage over the operating time. An operational plan is optimized to do so.
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.
Jaime Liew, Tuhfe Göçmen, Alan W. H. Lio, and Gunner Chr. Larsen
Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, https://doi.org/10.5194/wes-8-1387-2023, 2023
Short summary
Short summary
We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
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.
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.
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.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Iñaki Sandua-Fernández, Felipe Vittori, Raquel Martín-San-Román, Irene Eguinoa, and José Azcona-Armendáriz
Wind Energ. Sci., 8, 277–288, https://doi.org/10.5194/wes-8-277-2023, https://doi.org/10.5194/wes-8-277-2023, 2023
Short summary
Short summary
This work analyses in detail the causes of the yaw drift in floating offshore wind turbines with a single-point-mooring system induced by an upwind wind turbine. The ability of an individual pitch control strategy based on yaw misalignment is demonstrated through simulations using the NREL 5 MW wind turbine mounted on a single-point-mooring version of the DeepCwind OC4 floating platform. This effect is considered to be relevant for all single-point-moored concepts.
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.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
Short summary
Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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).
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
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.
Felipe Vittori, José Azcona, Irene Eguinoa, Oscar Pires, Alberto Rodríguez, Álex Morató, Carlos Garrido, and Cian Desmond
Wind Energ. Sci., 7, 2149–2161, https://doi.org/10.5194/wes-7-2149-2022, https://doi.org/10.5194/wes-7-2149-2022, 2022
Short summary
Short summary
This paper describes the results of a wave tank test campaign of a scaled SATH 10 MW INNWIND floating platform. The software-in-the-loop (SiL) hybrid method was used to include the wind turbine thrust and the in-plane rotor moments. Experimental results are compared with a numerical model developed in OpenFAST of the floating wind turbine. The results are discussed, identifying limitations of the numerical models and obtaining conclusions on how to improve them.
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.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
Short summary
Short summary
This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
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.
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.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
Edward Hart, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song
Wind Energ. Sci., 7, 1209–1226, https://doi.org/10.5194/wes-7-1209-2022, https://doi.org/10.5194/wes-7-1209-2022, 2022
Short summary
Short summary
We consider characteristics and drivers of loads experienced by wind turbine main bearings using simplified models of hub and main-bearing configurations. Influences of deterministic wind characteristics are investigated for 5, 7.5, and 10 MW turbine models. Load response to gusts and wind direction changes are also considered. Cubic load scaling is observed, veer is identified as an important driver of load fluctuations, and strong links between control and main-bearing load response are shown.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary
Short summary
Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022, https://doi.org/10.5194/wes-7-1093-2022, 2022
Short summary
Short summary
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects, reducing the power yield of the parks.
Mads Baungaard, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 783–800, https://doi.org/10.5194/wes-7-783-2022, https://doi.org/10.5194/wes-7-783-2022, 2022
Short summary
Short summary
Wind turbine wakes are dependent on the atmospheric conditions, and it is therefore important to be able to simulate in various different atmospheric conditions. This paper concerns the specific case of an unstable atmospheric surface layer, which is the lower part of the typical daytime atmospheric boundary layer. A simple flow model is suggested and tested for a range of single-wake scenarios, and it shows promising results for velocity deficit predictions.
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.
Paul Hulsman, Martin Wosnik, Vlaho Petrović, Michael Hölling, and Martin Kühn
Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, https://doi.org/10.5194/wes-7-237-2022, 2022
Short summary
Short summary
Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.
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.
Eric Simley, Paul Fleming, Nicolas Girard, Lucas Alloin, Emma Godefroy, and Thomas Duc
Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, https://doi.org/10.5194/wes-6-1427-2021, 2021
Short summary
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.
Vinit Dighe, Dhruv Suri, Francesco Avallone, and Gerard van Bussel
Wind Energ. Sci., 6, 1263–1275, https://doi.org/10.5194/wes-6-1263-2021, https://doi.org/10.5194/wes-6-1263-2021, 2021
Short summary
Short summary
Ducted wind turbines (DWTs) can be used for energy harvesting in urban areas where non-uniform flows are caused by the presence of buildings or other surface discontinuities. For this reason, the aerodynamic performance of DWTs in yawed-flow conditions must be characterized. It is found that the duct cross-section camber offers not only insensitivity to yaw but also a gain in performance up to a specific yaw angle; thereafter any further increase in yaw results in a performance drop.
Mark Kelly, Søren Juhl Andersen, and Ásta Hannesdóttir
Wind Energ. Sci., 6, 1227–1245, https://doi.org/10.5194/wes-6-1227-2021, https://doi.org/10.5194/wes-6-1227-2021, 2021
Short summary
Short summary
Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Chengyu Wang, Filippo Campagnolo, Helena Canet, Daniel J. Barreiro, and Carlo L. Bottasso
Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, https://doi.org/10.5194/wes-6-961-2021, 2021
Short summary
Short summary
This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind turbine model operated in a large boundary layer wind tunnel. A detailed scaling analysis accompanied by large-eddy simulations shows that these wakes are very realistic scaled versions of the ones generated by the parent full-scale wind turbine in the field.
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.
Maarten Paul van der Laan, Mark Kelly, and Mads Baungaard
Wind Energ. Sci., 6, 777–790, https://doi.org/10.5194/wes-6-777-2021, https://doi.org/10.5194/wes-6-777-2021, 2021
Short summary
Short summary
Wind farms operate in the atmospheric boundary layer, and their performance is strongly dependent on the atmospheric conditions. We propose a simple model of the atmospheric boundary layer that can be used as an inflow model for wind farm simulations for isolating a number of atmospheric effects – namely, the change in wind direction with height and atmospheric boundary layer depth. In addition, the simple model is shown to be consistent with two similarity theories.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
Short summary
Short summary
This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
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.
Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio
Wind Energ. Sci., 6, 111–129, https://doi.org/10.5194/wes-6-111-2021, https://doi.org/10.5194/wes-6-111-2021, 2021
Short summary
Short summary
Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
Short summary
Short summary
The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 5, 1537–1550, https://doi.org/10.5194/wes-5-1537-2020, https://doi.org/10.5194/wes-5-1537-2020, 2020
Short summary
Short summary
A new method is described to identify the aerodynamic characteristics of blade airfoils directly from operational data of the turbine. Improving on a previously published approach, the present method is based on a new maximum likelihood formulation that includes errors both in the outputs and the inputs. The method is demonstrated on the identification of the polars of small-scale turbines for wind tunnel testing.
Filippo Campagnolo, Robin Weber, Johannes Schreiber, and Carlo L. Bottasso
Wind Energ. Sci., 5, 1273–1295, https://doi.org/10.5194/wes-5-1273-2020, https://doi.org/10.5194/wes-5-1273-2020, 2020
Short summary
Short summary
The performance of an open-loop wake-steering controller is investigated with a new wind tunnel experiment. Three scaled wind turbines are placed on a large turntable and exposed to a turbulent inflow, resulting in dynamically varying wake interactions. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity and the existence of possible margins for improvement by the use of dynamic controllers.
Cited articles
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
Ainslie, J.: Calculating the flowfield in the wake of wind turbines, J.
Wind Eng. Ind. Aerod., 27, 213–224,
https://doi.org/10.1016/0167-6105(88)90037-2, 1988. a, b
Allaerts, D. and Meyers, J.: Large eddy simulation of a large wind-turbine
array in a conventionally neutral atmospheric boundary layer, Phys.
Fluids, 27, 065108, https://doi.org/10.1063/1.4922339, 2015. a, b, c
Andersen, S., Madariaga, A., Merz, K., Meyers, J., Munters, W., and Rodriguez, C.: Reference Wind Power Plant D1.03, Deliverable of EU H2020 TotalControl Project no. 727680, https://backend.orbit.dtu.dk/ws/portalfiles/portal/164663085/TotalControl_D1_03_Reference_Wind_Farm.pdf (last access: 21 July 2022), 2018. a
Andersen, S. J. and Troldborg, N.: Description of TotalControl Reference Wind
Farm Simulations, DTU [data set], https://doi.org/10.11583/DTU.13160606.v2, 2020. a
Andersen, S. J., Meyers, J., Munters, W., Sood, I., and Troldborg, N.: Flow
Database for reference wind farms part 1: precursor simulations, Tech. rep.,
DTU, KUL,
https://www.totalcontrolproject.eu/-/media/sites/totalcontrol/publications/public-deliverables/totalcontrol_d1_04_final.pdf?la=da&hash=B0047962A8135A4A8E75B01E3B0B8FE6D3DCD268 (last access: 21 July 2022),
2019. a
Andersson, L. E., Anaya-Lara, O., Tande, J. O., Merz, K. O., and Imsland, L.:
Wind farm control – Part I: A review on control system concepts and
structures, IET Renew. Power Gen., 15, 2085–2108,
https://doi.org/10.1049/rpg2.12160, 2021. a
Annoni, J., Fleming, P., Scholbrock, A., Roadman, J., Dana, S., Adcock, C., Porte-Agel, F., Raach, S., Haizmann, F., and Schlipf, D.: Analysis of control-oriented wake modeling tools using lidar field results, Wind Energ. Sci., 3, 819–831, https://doi.org/10.5194/wes-3-819-2018, 2018. a
Archer, C. L. and Vasel-Be-Hagh, A.: Wake steering via yaw control in
multi-turbine wind farms: Recommendations based on large-eddy simulation,
Sustainable Energy Technologies and Assessments, 33, 34–43,
https://doi.org/10.1016/j.seta.2019.03.002, 2019. a, b
Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L. C., Natarajan,
A., and Hartvig Hansen, M.: The DTU 10-MW Reference Wind Turbine, Danish Wind Power Research 2013, 27–28 May 2013, p. 138, https://orbit.dtu.dk/files/55645274/The_DTU_10MW_Reference_Turbine_Christian_Bak.pdf
(last access: 21 July 2022), 2013. a, b, c
Bartl, J., Mühle, F., and Sætran, L.: Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines, Wind Energ. Sci., 3, 489–502, https://doi.org/10.5194/wes-3-489-2018, 2018a. a
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, 2018b. 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
Becker, M., Allaerts, D., and van Wingerden, J. W.: FLORIDyn – A dynamic and flexible framework for real-time wind farm control, J. Phys.-Conf. Ser., 2265, 032103, https://doi.org/10.1088/1742-6596/2265/3/032103,
2022a. a, b
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. Discuss. [preprint], https://doi.org/10.5194/wes-2021-154, in review, 2022b. a, b
Biscani, F. and Izzo, D.: A parallel global multiobjective framework for
optimization: pagmo, Journal of Open Source Software, 5, 2338,
https://doi.org/10.21105/joss.02338, 2020. a, b
Blondel, F. and Cathelain, M.: An alternative form of the super-Gaussian wind turbine wake model, Wind Energ. Sci., 5, 1225–1236, https://doi.org/10.5194/wes-5-1225-2020, 2020. a
Bossanyi, E. and Ruisi, R.: Axial induction controller field test at Sedini wind farm, Wind Energ. Sci., 6, 389–408, https://doi.org/10.5194/wes-6-389-2021, 2021. a
Bottasso, C. L. and Campagnolo, F.: Handb. of Wind Energy Aerodyn, chap. Wind
Tunnel Testing of Wind Turbines and Farms, Springer, Print ISBN 978-3-030-31306-7,
Electronic ISBN 978-3-030-31307-4,
https://doi.org/10.1007/978-3-030-31307-4_54, 2021. a
Calaf, M., Meneveau, C., and Meyers, J.: Large eddy simulation study of fully
developed wind-turbine array boundary layers, Phys. Fluids, 22,
015110, https://doi.org/10.1063/1.3291077, 2010. a, b
Campagnolo, F., Weber, R., Schreiber, J., and Bottasso, C. L.: Wind tunnel testing of wake steering with dynamic wind direction changes, Wind Energ. Sci., 5, 1273–1295, https://doi.org/10.5194/wes-5-1273-2020, 2020. a
CL-Windcon: Research data, http://www.clwindcon.eu/research-data/ (last access: 21 July 2022),
2019. a
Crespo, A. and Hernández, J.: Turbulence characteristics in wind-turbine
wakes, J. Wind Eng. Ind. Aerod., 61, 71–85,
https://doi.org/10.1016/0167-6105(95)00033-X, 1996. a, b, c, d
Doekemeijer, B., Gomez-Iradi, S., Férnandez, L., Chavez, R., Raach, S.,
Campagnolo, F., Wang, C., Knudsen, T., Engels, W., Reyes-Báez, R., Andueza,
I., and van Wingerden, J.-W.: CL-Windcon D3.5: Demonstration of combined
turbine/farm level controls by simulations, Tech. rep.,
https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ca8fe2c1&appId=PPGMS (last access: 21 July 2022),
2019. a
Doekemeijer, B. M., Bossanyi, E.,
Bot, E. T. G., Kanev, S. K.,
Elorza, I., Campagnolo, F., Fortes‐Plaza, A.,
Schreiber, J., Eguinoa‐Erdozain, I.,
Gomez‐Iradi, S., Astrain‐Juangarcia, D.,
Cantero‐Nouqueret, E., Irigoyen‐Martinez, U.,
Fernandes‐Correia, P. M., Benito, P.,
Kern, S., Kim, Y., Raach, S., Knudsen, T., and Schito, P.: CL-Windcon D1.2: Description of the reference and the
control-oriented wind farm models, Tech. rep.,
http://www.clwindcon.eu/wp-content/uploads/2017/03/CL-Windcon-D1.2-Wind-farm-models.pdf (last access: 21 July 2022),
2018. 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, 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
Doubrawa, P., Quon, E. W., Martinez-Tossas, L. A., Shaler, K., Debnath, M.,
Hamilton, N., Herges, T. G., Maniaci, D., Kelley, C. L., Hsieh, A. S.,
Blaylock, M. L., van der Laan, P., Andersen, S. J., Krueger, S., Cathelain,
M., Schlez, W., Jonkman, J., Branlard, E., Steinfeld, G., Schmidt, S.,
Blondel, F., Lukassen, L. J., and Moriarty, P.: Multimodel validation of
single wakes in neutral and stratified atmospheric conditions, Wind Energy,
23, 2027–2055, https://doi.org/10.1002/we.2543, 2020. a
Draper, M., Guggeri, A., López, B., Díaz, A., Campagnolo, F., and
Usera, G.: A Large Eddy Simulation framework to assess wind farm power
maximization strategies: Validation of maximization by yawing, J.
Phys.-Conf. Ser., 1037, 072051,
https://doi.org/10.1088/1742-6596/1037/7/072051, 2018. a
DTU Wind Energy: PyWakeEllipSys,
https://topfarm.pages.windenergy.dtu.dk/cuttingedge/pywake/pywake_ellipsys/ (last access: 21 July 2022),
2021. a
FarmConners: Paving the Way for Wind Farm Control in Industry,
https://www.windfarmcontrol.info/ (last access: 21 July 2022), 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
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
Fleming, P. A., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K.,
Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Evaluating
techniques for redirecting turbine wakes using SOWFA, Renew. Energ., 70,
211–218, https://doi.org/10.1016/j.renene.2014.02.015, 2014. a, b
Gala-Santos, M. L.: Aerodynamics and Wind-Field Models for Wind Turbine
Control, PhD, University of Strathclyde, https://doi.org/10.48730/94sz-1f50, 2018. a, b, c
Göçmen, T., Kölle, K., Gancarski, P., and Petrović, V.:
FarmConners Wind Farm Control Benchmark documentation,
https://farmconners.readthedocs.io/en/latest/contact_us.html
last access: 21 July 2022. a
Gomez-Iradi, S., Férnandez, L., Chavez, R., and Pires, Ã.: CL-Windcon high
fidelity SOWFA simulations dataset, Tech. rep.,
Zenodo, [data set], https://doi.org/10.5281/zenodo.3889720, 2019. a
Göçmen, T., Kölle, K., Andersen, S. J., Eguinoa, I., Duc, T.,
Campagnolo, F., Iribas-Latour, M., Astrain, D., Bottasso, C., Meyers, J., van
Wingerden, J. W., and Giebel, G.: Launch of the FarmConners Wind Farm
Control benchmark for code comparison, J. Phys.-Conf. Ser.,
1618, 022040, https://doi.org/10.1088/1742-6596/1618/2/022040, 2020a. a
Göçmen, T., Larsén, X. G., and Imberger, M.: The effects of Open
Cellular Convection on Wind Farm Operation and Wakes, J. Phys.-Conf. Ser., 1618, 062014, https://doi.org/10.1088/1742-6596/1618/6/062014,
2020b. a
Göçmen, T., Kölle, K., Petrovic, V., and Gancarski, P.: FarmConners Wind
Farm Control Benchmark Repository, Zenodo [code], https://doi.org/10.5281/zenodo.5786988, 2021. a, b
Hulsman, P., Andersen, S. J., and Göçmen, T.: Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations, Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, 2020. a, b
INNWIND.EU: D1.21: Definition of the reference Wind Turbine-Analysis of Rotor
Design Parameters, http://www.innwind.eu/-/media/Sites/innwind/Publications/Deliverables/ (last access: 21 July 2022),
2013. a
International Electrotechnical Commission: Wind turbines-Part 12-1:
Power performance measurements of electricity producing wind turbines, IEC
61400-12-1, 2005. a
Ishihara, T. and Qian, G.-W.: A new Gaussian-based analytical wake model for
wind turbines considering ambient turbulence intensities and thrust
coefficient effects, J. Wind Eng. Ind. Aerod.,
177, 275–292, https://doi.org/10.1016/j.jweia.2018.04.010, 2018. a, b
Jiménez, Ã., Crespo, A., and Migoya, E.: Application of a LES technique to
characterize the wake deflection of a wind turbine in yaw, Wind Energy, 13,
559–572, https://doi.org/10.1002/we.380, 2010. a, b
Jonkman, J.: FAST v8 and the transition to OpenFAST, Tech. rep., NREL,
https://www.nrel.gov/wind/nwtc/fast.html (last access: 21 July 2022), 2017. a
Kheirabadi, A. C. and Nagamune, R.: A quantitative review of wind farm control
with the objective of wind farm power maximization, J. Wind
Eng. Ind. Aerod., 192, 45–73,
https://doi.org/10.1016/j.jweia.2019.06.015, 2019. a, b
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
Krogstad, P.-Ã. and Adaramola, M. S.: Performance and near wake measurements of
a model horizontal axis wind turbine, Wind Energy, 15, 743–756,
https://doi.org/10.1002/we.502, 2012. a
Larsen, G., Ott, S., Liew, J., van der Laan, M., Simon, E., Thorsen, G., and
Jacobs, P.: Yaw induced wake deflection-a full-scale validation study,
J. Phys.-Conf. Ser., 1618, 062047,
https://doi.org/10.1088/1742-6596/1618/6/062047, 2020. a, b, c, d
Larsen, T. J. and Hansen, A. M.: How 2 HAWC2, the user's manual, target, 2,
Risø National Laboratory, Roskilde, Denmark,
70 pp.,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/7703110/ris_r_1597.pdf
(last access: 21 July 2022), ISBN Print 978-87-550-3583-6, 2007. a
Liew, J., Pirrung, G. R., and Urbán, A. M.: Effect of varying fidelity
turbine models on wake loss prediction, J. Phys.-Conf.
Ser., 1618, 062002, https://doi.org/10.1088/1742-6596/1618/6/062002, 2020a. 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, 2020b. a, b
Madsen, H. A., Larsen, G. C., Larsen, T. J., Troldborg, N., and Mikkelsen, R.:
Calibration and validation of the dynamic wake meandering model for
implementation in an aeroelastic code, J. Sol. Energ.-T, ASME,
132, 041014, https://doi.org/10.1115/1.4002555, 2010. a, b
Madsen, H. A., Larsen, T. J., Pirrung, G. R., Li, A., and Zahle, F.: Implementation of the blade element momentum model on a polar grid and its aeroelastic load impact, Wind Energ. Sci., 5, 1–27, https://doi.org/10.5194/wes-5-1-2020, 2020. a
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. Discuss. [preprint], https://doi.org/10.5194/wes-2022-24, in review, 2022. a
Michelsen, J. A.: Basis3D – a Platform for Development of Multiblock PDE
Solvers, Tech. rep., Technical University of Denmark,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/272917945/Michelsen_J_Basis3D.pdf
(last access: 21 July 2022), 1992. a
Michelsen, J. A.: Block structured Multigrid solution of 2D and 3D elliptic
PDE's, Tech. Rep. AFM, Dept. of Fluid Mechanics Technical University of Denmark,
https://books.google.dk/books?id=dDu-XwAACAAJ
(last access: 21 July 2022), 1994. a
Morgan, K., Hughes, T., and Taylor, C.: Investigation of a mixing length and a
two-equation turbulence model utilizing the finite element method, Appl.
Math. Model., 1, 395–400, 1977. a
Moriarty, P., Rodrigo, J. S., Gancarski, P., Chuchfield, M., Naughton, J. W.,
Hansen, K. S., Machefaux, E., Maguire, E., Castellani, F., Terzi, L., Breton,
S.-P., and Ueda, Y.: IEA-Task 31 WAKEBENCH: Towards a protocol for wind
farm flow model evaluation. Part 2: Wind farm wake models, J.
Phys.-Conf. Ser., 524, 012185,
https://doi.org/10.1088/1742-6596/524/1/012185, 2014. a
Munters, W. and Meyers, J.: Dynamic Strategies for Yaw and Induction Control of
Wind Farms Based on Large-Eddy Simulation and Optimization, Energies, 11, 177,
https://doi.org/10.3390/en11010177, 2018. a, b, c
Munters, W., Meneveau, C., and Meyers, J.: Turbulent Inflow Precursor Method
with Time-Varying Direction for Large-Eddy Simulations and Applications to
Wind Farms, Bound.-Lay. Meteorol., 159, 305–328,
https://doi.org/10.1007/s10546-016-0127-z, 2016. a
Munters, W., Sood, I., and Meyers, J.: Precursor dataset PDk, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650100, 2019a. a
Munters, W., Sood, I., and Meyers, J.: Precursor dataset PDkhi, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650102, 2019b. a
Munters, W., Sood, I., and Meyers, J.: Precursor dataset CNk2, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650096, 2019c. a
Munters, W., Sood, I., and Meyers, J.: Precursor dataset CNk4, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650098, 2019d. a
Neilson, V. W.: Individual Blade Control for Fatigue Load Reduction of
Large-scaled Wind Turbines : Theory and Modelling, MS, University of
Strathclyde, 2010. a
Niayifar, A. and Porté-Agel, F.: Analytical modeling of wind farms: A new
approach for power prediction, Energies, 9, 741, https://doi.org/10.3390/en9090741, 2016. a
OpenFOAM: OpenFOAM, the open source CFD toolbox,
http://www.openfoam.com/ (last access: 21 July 2022), 2013. a
Øye, S.: FLEX4 simulation of wind turbine dynamics, in: Proceedings of the 28th IEA Meeting of Experts Concerning State of the Art of Aeroelastic Codes for Wind Turbine Calculations, available through International Energy Agency, 1996. a
Poushpas, S.: Wind farm simulation modelling and control, PhD thesis,
University of Strathclyde, https://doi.org/10.48730/tm7n-6v93, 2016. a, b
Qian, G.-W. and Ishihara, T.: A New Analytical Wake Model for Yawed Wind
Turbines, Energies, 11, 665, https://doi.org/10.3390/en11030665, 2018. a, b
Qian, G.-W. and Ishihara, T.: Wind farm power maximization through wake
steering with a new multiple wake model for prediction of turbulence
intensity, Energy, 220, 119680, https://doi.org/10.1016/j.energy.2020.119680, 2021. a
Recalde-Camacho, L., Stock, A., Giles, A. D., and Leithead, W.: Control of Aeroelastically-tailored Wind Turbines, University of Strathclyde, Glasgow,
https://strathprints.strath.ac.uk/77283/ (last access: 21 July 2022), 2020. a
Rockel, S., Peinke, J., Hölling, M., and Cal, R. B.: Dynamic wake development
of a floating wind turbine in free pitch motion subjected to turbulent inflow
generated with an active grid, Renew. Energ., 112, 1–16,
https://doi.org/10.1016/j.renene.2017.05.016, 2017. a
Rodrigo, J. S., Gancarski, P., Arroyo, R. C., Moriarty, P., Chuchfield, M.,
Naughton, J. W., Hansen, K. S., Machefaux, E., Koblitz, T., Maguire, E.,
Castellani, F., Terzi, L., Breton, S.-P., Ueda, Y., Prospathopoulos, J.,
Oxley, G. S., Peralta, C., Zhang, X., and Witha, B.: IEA-Task 31
WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 1:
Flow-over-terrain models, J. Phys.-Conf. Ser., 524,
012105, https://doi.org/10.1088/1742-6596/524/1/012105, 2014. a
Ruisi, R. and Bossanyi, E.: Engineering models for turbine wake velocity
deficit and wake deflection. A new proposed approach for onshore and offshore
applications, J. Phys.-Conf. Ser., 1222, 012004,
https://doi.org/10.1088/1742-6596/1222/1/012004, 2019. a, b
Schreiber, J., Nanos, E. M., Campagnolo, F., and Bottasso, C. L.: Verification
and Calibration of a Reduced Order Wind Farm Model by Wind Tunnel
Experiments, J. Phys.-Conf. Ser., 854, 012041,
https://doi.org/10.1088/1742-6596/854/1/012041, 2017. a
Schreiber, J., Bottasso, C. L., Salbert, B., and Campagnolo, F.: Improving wind farm flow models by learning from operational data, Wind Energ. Sci., 5, 647–673, https://doi.org/10.5194/wes-5-647-2020, 2020. a, b
Shao, Z., Wu, Y., Li, L., Han, S., and Liu, Y.: Multiple wind turbine wakes
modeling considering the faster wake recovery in overlapped wakes, Energies,
12, 680, https://doi.org/10.3390/en12040680, 2019. a
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020. 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, b, c, d, e, f, g, h
Sørensen, J. N. and Shen, W. Z.: Numerical modeling of wind turbine wakes,
Journal of Fluids Engineering, T. ASME, 124, 393–399,
https://doi.org/10.1115/1.1471361, 2002. a
Sørensen, J. N., Mikkelsen, R. F., Henningson, D. S., Ivanell, S., Sarmast,
S., and Andersen, S. J.: Simulation of wind turbine wakes using the actuator
line technique, Philos. T. R. Soc. A, 373, 20140071,
https://doi.org/10.1098/rsta.2014.0071, 2015. a
Stevens, R. J., Graham, J., and Meneveau, C.: A concurrent precursor inflow
method for Large Eddy Simulations and applications to finite length wind
farms, Renew. Energ., 68, 46–50, https://doi.org/10.1016/j.renene.2014.01.024,
2014.
a
van Beek, M. T., Viré, A., and Andersen, S. J.: Sensitivity and Uncertainty of
the FLORIS Model Applied on the Lillgrund Wind Farm, Energies, 14, 1293,
https://doi.org/10.3390/en14051293, 2021. a, b
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, b
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, 2022. a, b
van der Laan, M. P., Sørensen, N. N., Réthoré, P. E., Mann, J.,
Kelly, M. C., Troldborg, N., Schepers, J. G., and Machefaux, E.: An improved
k-ϵ model applied to a wind turbine wake in atmospheric
turbulence, Wind Energy, 18, 889–907, https://doi.org/10.1002/we.1736, 2015. a
van Wingerden, J. W., Fleming, P. A., Göçmen, 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, b
Verelst, D. R., Hansen, M. H., and Pirrung, G.: Steady State Comparisons HAWC2
v12. 5 vs HAWCStab2 v2. 14: Integrated and distributed aerodynamic
performance, DTU Wind Energy, 172, https://backend.orbit.dtu.dk/ws/portalfiles/portal/152024055/DTU_Wind_Energy_E_0172_steady_state_h2_215_vs_hs2_214.pdf
(last access: 21 July 2022), ISBN 978-87-93549-40-1, 2018. a
Vitsas, A. and Meyers, J.: Multiscale aeroelastic simulations of large wind
farms in the atmospheric boundary layer, J. Phys.-Conf.
Ser., 753, 082020, https://doi.org/10.1088/1742-6596/753/8/082020, 2016. a
Wieners, C.: Taylor-Hood elements in 3D, in: Analysis and Simulation of
Multifield Problems, edited by: Wendland, W.
and Efendiev, M., Springer, 189–196, ISBN 978-3-540-36527-3, 2003. a
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.
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control...
Altmetrics
Final-revised paper
Preprint