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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Johannes Schreiber, Carlo L. Bottasso, Bastian Salbert, and Filippo Campagnolo
Wind Energ. Sci., 5, 647–673, https://doi.org/10.5194/wes-5-647-2020, https://doi.org/10.5194/wes-5-647-2020, 2020
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The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are
learnedfrom operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.
Jaime Liew, Albert M. Urbán, and Søren Juhl Andersen
Wind Energ. Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, https://doi.org/10.5194/wes-5-427-2020, 2020
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In wind farms, the interaction between neighboring turbines can cause notable power losses. The focus of the paper is on how the combination of turbine yaw misalignment and wake effects influences the power loss in a wind turbine. The results of the paper show a more notable power loss due to turbine misalignment when turbines are closely spaced. The presented conclusions enable better predictions of a turbine's power production, which can assist the wind farm design process.
Maarten Paul van der Laan, Mark Kelly, Rogier Floors, and Alfredo Peña
Wind Energ. Sci., 5, 355–374, https://doi.org/10.5194/wes-5-355-2020, https://doi.org/10.5194/wes-5-355-2020, 2020
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The design of wind turbines and wind farms can be improved by increasing the accuracy of the inflow models representing the atmospheric boundary layer (ABL). In this work we employ numerical simulations of the idealized ABL, which can represent the mean effects of Coriolis and buoyancy forces and surface roughness. We find a new model-based similarity that provides a better understanding of the idealized ABL. In addition, we extend the model to include effects of convective buoyancy forces.
Björn Maronga, Sabine Banzhaf, Cornelia Burmeister, Thomas Esch, Renate Forkel, Dominik Fröhlich, Vladimir Fuka, Katrin Frieda Gehrke, Jan Geletič, Sebastian Giersch, Tobias Gronemeier, Günter Groß, Wieke Heldens, Antti Hellsten, Fabian Hoffmann, Atsushi Inagaki, Eckhard Kadasch, Farah Kanani-Sühring, Klaus Ketelsen, Basit Ali Khan, Christoph Knigge, Helge Knoop, Pavel Krč, Mona Kurppa, Halim Maamari, Andreas Matzarakis, Matthias Mauder, Matthias Pallasch, Dirk Pavlik, Jens Pfafferott, Jaroslav Resler, Sascha Rissmann, Emmanuele Russo, Mohamed Salim, Michael Schrempf, Johannes Schwenkel, Gunther Seckmeyer, Sebastian Schubert, Matthias Sühring, Robert von Tils, Lukas Vollmer, Simon Ward, Björn Witha, Hauke Wurps, Julian Zeidler, and Siegfried Raasch
Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, https://doi.org/10.5194/gmd-13-1335-2020, 2020
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In this paper, we describe the PALM model system 6.0. PALM is a Fortran-based turbulence-resolving code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. During the last years, PALM has been significantly improved and now offers a variety of new components that are especially designed to simulate the urban atmosphere at building-resolving resolution.
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
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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
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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.
Róbert Ungurán, Vlaho Petrović, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci., 4, 677–692, https://doi.org/10.5194/wes-4-677-2019, https://doi.org/10.5194/wes-4-677-2019, 2019
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A novel lidar-based sensory system for wind turbine control is proposed. The main contributions are the parametrization method of the novel measurement system, the identification of possible sources of measurement uncertainty, and their modelling. Although not the focus of the submitted paper, the mentioned contributions represent essential building blocks for robust feedback–feedforward wind turbine control development which could be used to improve wind turbine control strategies.
Maarten Paul van der Laan, Søren Juhl Andersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 4, 645–651, https://doi.org/10.5194/wes-4-645-2019, https://doi.org/10.5194/wes-4-645-2019, 2019
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Wind farm layouts are designed by simple engineering wake models, which are fast to compute but also include a high uncertainty. Higher-fidelity models, such as Reynolds-averaged Navier–Stokes, can be used to verify optimized wind farm layouts, although the computational costs are high due to the large number of cases that are needed to calculate the annual energy production. This article presents a new wind turbine control method to speed up the high-fidelity simulations by a factor of 2–3.
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
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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
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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.
Vinit V. Dighe, Francesco Avallone, Ozer Igra, and Gerard van Bussel
Wind Energ. Sci., 4, 439–449, https://doi.org/10.5194/wes-4-439-2019, https://doi.org/10.5194/wes-4-439-2019, 2019
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Multi-element ducts are investigated to further improve the aerodynamic performance of ducted wind turbines. CFD simulations are performed for a multi-element duct geometry consisting of a duct and a flap; the goal is to evaluate the effects on the aerodynamic performance of the radial gap length and the deflection angle of the flap. Increasing the radial gap length results in an augmentation of the total thrust generated by the DWT, whereas a larger deflection angle has an opposite effect.
Thomas Duc, Olivier Coupiac, Nicolas Girard, Gregor Giebel, and Tuhfe Göçmen
Wind Energ. Sci., 4, 287–302, https://doi.org/10.5194/wes-4-287-2019, https://doi.org/10.5194/wes-4-287-2019, 2019
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Wind turbine wake recovery is very sensitive to ambient atmospheric conditions. This paper presents a way of including a local turbulence intensity estimation from SCADA into the Jensen wake model to improve its accuracy. This new model procedure is used to optimize power production of an operating wind farm and shows that some gains can be expected even if uncertainties remain high. These optimized settings are to be implemented in a field test campaign in the scope of the SMARTEOLE project.
Maarten Paul van der Laan, Søren Juhl Andersen, Néstor Ramos García, Nikolas Angelou, Georg Raimund Pirrung, Søren Ott, Mikael Sjöholm, Kim Hylling Sørensen, Julio Xavier Vianna Neto, Mark Kelly, Torben Krogh Mikkelsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 251–271, https://doi.org/10.5194/wes-4-251-2019, https://doi.org/10.5194/wes-4-251-2019, 2019
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Over the past few decades, single-rotor wind turbines have increased in size with the blades being extended toward lengths of 100 m. An alternative upscaling of turbines can be achieved by using multi-rotor wind turbines. In this article, measurements and numerical simulations of a utility-scale four-rotor wind turbine show that rotor interaction leads to increased energy production and faster wake recovery; these findings may allow for the design of wind farms with improved energy production.
Mehdi Vali, Vlaho Petrović, Gerald Steinfeld, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci., 4, 139–161, https://doi.org/10.5194/wes-4-139-2019, https://doi.org/10.5194/wes-4-139-2019, 2019
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A new active power control (APC) approach is investigated to simultaneously reduce the wake-induced power tracking errors and structural fatigue loads of individual turbines within a wind farm. The non-unique solution of the APC problem with respect to the distribution of the individual powers is exploited. The simple control architecture and practical measurement system make the proposed approach prominent for real-time control of large wind farms with turbulent flows and wakes.
Jiangang Wang, Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 4, 71–88, https://doi.org/10.5194/wes-4-71-2019, https://doi.org/10.5194/wes-4-71-2019, 2019
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This paper describes an LES approach for the simulation of wind
turbines and their wakes. The simulation model is used to
develop a complete digital copy of experiments performed with
scaled wind turbines in a boundary layer wind tunnel, including the
passive generation of a sheared turbulent flow. Numerical results
are compared with experimental measurements, with a good overall
matching between the two.
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
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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
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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
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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.
Georg Raimund Pirrung and Maarten Paul van der Laan
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-59, https://doi.org/10.5194/wes-2018-59, 2018
Revised manuscript not accepted
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Wind turbine loading decreases towards the blade tip due to the velocities induced by the tip vortex and the spanwise flow. It has been shown that the tip loss factor applied on the aerodynamic forces should be different for the axial and tangential loading of the turbine due to the rotation of the resulting force vector caused by the induced velocity. The present article contains the derivation of a simple correction for the tangential load factor that takes this rotation into account.
Jiangang Wang, Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-47, https://doi.org/10.5194/wes-2018-47, 2018
Revised manuscript has not been submitted
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This paper describes a Scale Adaptive Simulation (SAS) approach for
the numerical simulation of wind turbines and their wakes. The SAS
formulation is found to be about one order of magnitude faster than
a classical LES approach. The simulation models are compared to
each other and with experimental measurements obtained with scaled
wind turbines in a boundary layer wind tunnel.
Wim Munters and Johan Meyers
Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, https://doi.org/10.5194/wes-3-409-2018, 2018
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Wake interactions in wind farms result in power losses for downstream turbines. We aim to mitigate these losses through coordinated control of the induced slowdown of the wind by each turbine. We further analyze results from earlier work towards the utilization of such control strategies in practice. Coherent vortex shedding is identified and mimicked by a sinusoidal control. The latter is shown to increase power in downstream turbines and is robust to turbine spacing and turbulence intensity.
Jakob Mann, Alfredo Peña, Niels Troldborg, and Søren J. Andersen
Wind Energ. Sci., 3, 293–300, https://doi.org/10.5194/wes-3-293-2018, https://doi.org/10.5194/wes-3-293-2018, 2018
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Turbulence is usually assumed to be unmodified by the stagnation occurring in front of a wind turbine rotor. All manufacturers assume this in their dynamic load calculations. If this assumption is not true it might bias the load calculations and the turbines might not be designed optimally. We investigate the assumption with a Doppler lidar measuring forward from the top of the nacelle and find small but systematic changes in the approaching turbulence that depend on the power curve.
Alfredo Peña, Kurt Schaldemose Hansen, Søren Ott, and Maarten Paul van der Laan
Wind Energ. Sci., 3, 191–202, https://doi.org/10.5194/wes-3-191-2018, https://doi.org/10.5194/wes-3-191-2018, 2018
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We analyze the wake of the Anholt offshore wind farm in Denmark by intercomparing models and measurements. We also look at the effect of the land on the wind farm by intercomparing mesoscale winds and measurements. Annual energy production and capacity factor estimates are performed using different approaches. Lastly, the uncertainty of the wake models is determined by bootstrapping the data; we find that the wake models generally underestimate the wake losses.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Søren Juhl Andersen
Wind Energ. Sci., 3, 121–138, https://doi.org/10.5194/wes-3-121-2018, https://doi.org/10.5194/wes-3-121-2018, 2018
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The wind speed measured by a flow sensor mounted on the blade of a wind turbine is disturbed by the turbine. This paper presents a method to obtain the free turbulence inflow by compensating for this disturbance.
The method is tested using numerical simulations and can be used to extract inflow information for accurate aeroelastic load simulations.
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
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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.
Carl R. Shapiro, Johan Meyers, Charles Meneveau, and Dennice F. Gayme
Wind Energ. Sci., 3, 11–24, https://doi.org/10.5194/wes-3-11-2018, https://doi.org/10.5194/wes-3-11-2018, 2018
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We investigate the capability of wind farms to track a power reference signal to help ensure reliable power grid operations. The wind farm controller is based on a simple dynamic wind farm model and tested using high-fidelity simulations. We find that the dynamic nature of the wind farm model is vital for tracking the power signal, and the controlled wind farm would pass industry performance tests in most cases.
Lukas Vollmer, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 2, 603–614, https://doi.org/10.5194/wes-2-603-2017, https://doi.org/10.5194/wes-2-603-2017, 2017
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A model chain to simulate changing atmospheric conditions at the location of an offshore wind farm is introduced and validated. The methodology is used to simulate the wind flow upstream and downstream of an offshore wind turbine of the German wind farm Alpha ventus. The model results show a good agreement with wind measurements from the met mast that is located at the wind farm and with remote sensing measurements of the horizontal wind field.
Marijn Floris van Dooren, Filippo Campagnolo, Mikael Sjöholm, Nikolas Angelou, Torben Mikkelsen, and Martin Kühn
Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017, https://doi.org/10.5194/wes-2-329-2017, 2017
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We conducted measurements in a wind tunnel with the remote sensing technique lidar to map the flow around a row of three model wind turbines. Two lidars were positioned near the wind tunnel walls to measure the two-dimensional wind vector over a defined scanning line or area without influencing the flow itself. A comparison of the lidar measurements with a hot-wire probe and a thorough uncertainty analysis confirmed the usefulness of lidar technology for such flow measurements in a wind tunnel.
Maarten Paul van der Laan and Niels Nørmark Sørensen
Wind Energ. Sci., 2, 285–294, https://doi.org/10.5194/wes-2-285-2017, https://doi.org/10.5194/wes-2-285-2017, 2017
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In recent years, wind farms have grown in size and are more frequently placed in wind farm clusters. This means that large-scale effects such as the interaction of the Coriolis force and wind farm wakes are becoming more important for designing energy efficient wind farms. The literature disagrees on the turning direction of a wind farm wake due to the Coriolis force. In this article, we explain why the Coriolis force turns a wind farm wake clockwise in the Northern Hemisphere.
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
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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.
Maria Aparicio-Sanchez, Alvaro Gonzalez-Salcedp, Sugoi Gomez-Iradi, and Xabier Munduate
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2016-50, https://doi.org/10.5194/wes-2016-50, 2016
Preprint withdrawn
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The use of the flaps is one of the options to reduce the equivalent loads or to increase the power in large wind turbines. This paper presents a methodology to simulate the performance of the blades with these aerodynamic devices. This method presents important advantages with respect to CFD in terms of computational effort and leads to similar results.
Vahid S. Bokharaie, Pieter Bauweraerts, and Johan Meyers
Wind Energ. Sci., 1, 311–325, https://doi.org/10.5194/wes-1-311-2016, https://doi.org/10.5194/wes-1-311-2016, 2016
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Given a wind farm with known dimensions and number of wind turbines, we try to find the optimum positioning of wind turbines that maximises wind-farm energy production. We propose an optimisation approach that is based on a hybrid combination of large-eddy simulation (LES) and the Jensen model; in this approach optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the Jensen model.
David Bastine, Lukas Vollmer, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2016-38, https://doi.org/10.5194/wes-2016-38, 2016
Revised manuscript not accepted
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Modeling of wind turbine wakes plays a key role in the maximization of the power output and lifetime of wind turbines in wind farms. In order to capture important dynamic and turbulent aspects of the wake, a new stochastic modeling approach is presented in this work. The resulting new kind of stochastic wake model captures important characteristics of loads which act on wind turbines in the wake. It might therefore be of great use for the planing and controlling of wind farms.
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
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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.
Lukas Vollmer, Gerald Steinfeld, Detlev Heinemann, and Martin Kühn
Wind Energ. Sci., 1, 129–141, https://doi.org/10.5194/wes-1-129-2016, https://doi.org/10.5194/wes-1-129-2016, 2016
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The wake flow downstream of yaw misaligned wind turbines is studied in numeric simulations of different atmospheric turbulence and shear conditions. We find that the average trajectory of the wake as well as the variation about this average is influenced by the thermal stability of the atmosphere. The results suggest that an intentional intervention in the yaw control of individual turbines to increase overall wind farm performance might be not successful during unstable thermal conditions.
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: Dynamics and control | Topic: Wind turbine control
Brief communication: Real-time estimation of the optimal tip-speed ratio for controlling wind turbines with degraded blades
On the robustness of a blade-load-based wind speed estimator to dynamic pitch control strategies
The potential of wave feedforward control for floating wind turbines: a wave tank experiment
Assessing the impact of waves and platform dynamics on floating wind-turbine energy production
Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise
Feedforward pitch control for a 15 MW wind turbine using a spinner-mounted single-beam lidar
Wind vane correction during yaw misalignment for horizontal-axis wind turbines
Increased power gains from wake steering control using preview wind direction information
Analysis and multi-objective optimisation of wind turbine torque control strategies
Damping analysis of floating offshore wind turbines (FOWTs): a new control strategy reducing the platform vibrations
Assessing lidar-assisted feedforward and multivariable feedback controls for large floating wind turbines
Prognostics-based adaptive control strategy for lifetime control of wind turbines
Platform yaw drift in upwind floating wind turbines with single-point-mooring system and its mitigation by individual pitch control
Evaluation of lidar-assisted wind turbine control under various turbulence characteristics
Demonstration of a fault impact reduction control module for wind turbines
Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history
Devesh Kumar and Mario A. Rotea
Wind Energ. Sci., 9, 2133–2146, https://doi.org/10.5194/wes-9-2133-2024, https://doi.org/10.5194/wes-9-2133-2024, 2024
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The performance of a wind turbine is affected by blade surface degradation due to wear and tear, dirt, bugs, and icing. As blades degrade, optimal operating points such as the tip-speed ratio (TSR) can change. Re-tuning the TSR to its new optimal value can lead to recovery of energy losses under blade degradation. In this work, we utilize a real-time algorithm to re-tune the TSR to its new unknown optimal value under blade degradation and demonstrate energy gains using simulations.
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
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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.
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
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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.
Alessandro Fontanella, Giorgio Colpani, Marco De Pascali, Sara Muggiasca, and Marco Belloli
Wind Energ. Sci., 9, 1393–1417, https://doi.org/10.5194/wes-9-1393-2024, https://doi.org/10.5194/wes-9-1393-2024, 2024
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Waves can boost a floating wind turbine's power output by moving its rotor against the wind. Studying this, we used four models to explore the impact of waves and platform dynamics on turbines in the Mediterranean. We found that wind turbulence, not waves, primarily affects power fluctuations. In real conditions, floating wind turbines produce less energy compared to fixed-bottom ones, mainly due to platform tilt.
Alessandro Croce, Stefano Cacciola, and Federico Isella
Wind Energ. Sci., 9, 1211–1227, https://doi.org/10.5194/wes-9-1211-2024, https://doi.org/10.5194/wes-9-1211-2024, 2024
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For a few years now, various techniques have been studied to maximize the energy production of a wind farm, that is, from a system consisting of several wind turbines. These wind farm controller techniques are often analyzed individually and can generate loads higher than the design ones on the individual wind turbine. In this paper we study the simultaneous use of two different techniques with the goal of finding the optimal combination that at the same time preserves the design loads.
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
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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.
Wei Fu, Feng Guo, David Schlipf, and Alfredo Peña
Wind Energ. Sci., 8, 1893–1907, https://doi.org/10.5194/wes-8-1893-2023, https://doi.org/10.5194/wes-8-1893-2023, 2023
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A high-quality preview of the rotor-effective wind speed is a key element of the benefits of feedforward pitch control. We model a one-beam lidar in the spinner of a 15 MW wind turbine. The lidar rotates with the wind turbine and scans the inflow in a circular pattern, mimicking a multiple-beam lidar at a lower cost. We found that a spinner-based one-beam lidar provides many more control benefits than the one on the nacelle, which is similar to a four-beam nacelle lidar for feedforward control.
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
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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.
Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 8, 1693–1710, https://doi.org/10.5194/wes-8-1693-2023, https://doi.org/10.5194/wes-8-1693-2023, 2023
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Unexpected wind direction changes are undesirable, especially when performing wake steering. This study explores whether the yaw controller can benefit from accessing wind direction information before a change reaches the turbine. Results from two models with different fidelities demonstrate that wake steering can indeed benefit from preview information.
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
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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.
Matteo Capaldo and Paul Mella
Wind Energ. Sci., 8, 1319–1339, https://doi.org/10.5194/wes-8-1319-2023, https://doi.org/10.5194/wes-8-1319-2023, 2023
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The controller impacts the movements, loads and yield of wind turbines.
Standard controllers are not adapted for floating, and they can lead to underperformances and overloads. New control strategies, considering the coupling between the floating dynamics and the rotor dynamics, are necessary to reduce platform movements and to improve performances. This work proposes a new control strategy adapted to floating wind, showing a reduction in loads without affecting the power production.
Feng Guo and David Schlipf
Wind Energ. Sci., 8, 1299–1317, https://doi.org/10.5194/wes-8-1299-2023, https://doi.org/10.5194/wes-8-1299-2023, 2023
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This paper assesses lidar-assisted collective pitch feedforward (LACPF) and multi-variable feedback (MVFB) controls for the IEA 15.0 MW reference turbine. The main contributions of this work include (a) optimizing a four-beam pulsed lidar for a large turbine, (b) optimal tuning of speed regulation gains and platform feedback gains for the MVFB and LACPF controllers, and (c) assessing the benefits of the two control strategies using realistic offshore turbulence spectral characteristics.
Edwin Kipchirchir, M. Hung Do, Jackson G. Njiri, and Dirk Söffker
Wind Energ. Sci., 8, 575–588, https://doi.org/10.5194/wes-8-575-2023, https://doi.org/10.5194/wes-8-575-2023, 2023
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In this work, an adaptive control strategy for controlling the lifetime of wind turbine components is proposed. Performance of the lifetime controller is adapted based on real-time health status of the rotor blades to guarantee a predefined lifetime. It shows promising results in lifetime control of blades without speed regulation and tower load mitigation trade-off. It can be applied in optimizing maintenance scheduling of wind farms, which increases reliability and reduces maintenance costs.
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
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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.
Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 8, 149–171, https://doi.org/10.5194/wes-8-149-2023, https://doi.org/10.5194/wes-8-149-2023, 2023
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The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal model-based 4D turbulence, considering the variation of turbulence parameters. Simulations are performed for the above-rated mean wind speed, using the NREL 5.0 MW reference wind turbine and a four-beam lidar system. Using lidar-assisted control reduces the variations in rotor speed, pitch rate, tower base fore–aft bending moment, and electrical power significantly.
Benjamin Anderson and Edward Baring-Gould
Wind Energ. Sci., 7, 1753–1769, https://doi.org/10.5194/wes-7-1753-2022, https://doi.org/10.5194/wes-7-1753-2022, 2022
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Our article proposes an easy-to-integrate wind turbine control module which mitigates wind turbine fault conditions and sends predictive information to the grid operator, all while maximizing power production. This gives the grid operator more time to react to faults with its dispatch decisions, easing the transition between different generators. This study aims to illustrate the controller’s functionality under various types of faults and highlight potential wind turbine and grid benefits.
Stefan Loew and Carlo L. Bottasso
Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, https://doi.org/10.5194/wes-7-1605-2022, 2022
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This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
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...
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