Articles | Volume 8, issue 9
https://doi.org/10.5194/wes-8-1387-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-1387-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm
Jaime Liew
CORRESPONDING AUTHOR
Department of Wind and Energy Systems, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000 Roskilde, Denmark
Tuhfe Göçmen
Department of Wind and Energy Systems, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000 Roskilde, Denmark
Alan W. H. Lio
Department of Wind and Energy Systems, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000 Roskilde, Denmark
Gunner Chr. Larsen
Department of Wind and Energy Systems, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000 Roskilde, Denmark
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Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 1811–1826, https://doi.org/10.5194/wes-9-1811-2024, https://doi.org/10.5194/wes-9-1811-2024, 2024
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Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-96, https://doi.org/10.5194/wes-2024-96, 2024
Preprint under review for WES
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This research develops a new method for assessing Hybrid Power Plants (HPPs) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art Energy Management System (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
Jens Visbech, Tuhfe Göçmen, 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.
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.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
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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.
Liang Dong, Wai Hou Lio, and Eric Simley
Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, https://doi.org/10.5194/wes-6-1491-2021, 2021
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This paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of lidar-assisted control (LAC) in wind turbine design. The value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors.
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.
Mads M. Pedersen and Gunner C. Larsen
Wind Energ. Sci., 5, 1551–1566, https://doi.org/10.5194/wes-5-1551-2020, https://doi.org/10.5194/wes-5-1551-2020, 2020
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In this paper, the influence of optimal wind farm control and optimal wind farm layout is investigated in terms of power production. The capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm, Lillgrund. It shows that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.
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°.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 303–323, https://doi.org/10.5194/wes-4-303-2019, https://doi.org/10.5194/wes-4-303-2019, 2019
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In this paper, detailed inflow information extracted from measurements is used to improve the accuracy of simulated wind turbine fatigue loads. Inflow information from nearby met masts is utilised as well as information from a blade-mounted flow sensor in combination with a method to compensate for the disturbance to the flow caused by the presence of the wind turbine.
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.
Jens N. Sørensen and Gunner C. Larsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-53, https://doi.org/10.5194/wes-2018-53, 2018
Preprint withdrawn
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The work assesses the potential of a massive exploitation of offshore wind power in the North Sea by combining a meteorological model with a cost model including a bathymetric analysis of the water depth of the North Sea. As an overall finding, it is shown that the electrical power demand of Europe can be fulfilled by exploiting an area corresponding to about 1/3 of the North Sea with 100.000 wind turbines of generator size 13 MW on water depths up to 45 m to a cost price of about 7.5 €cents/kWh.
Mads M. Pedersen, Torben J. Larsen, Helge Aa. Madsen, and Gunner Chr. Larsen
Wind Energ. Sci., 2, 547–567, https://doi.org/10.5194/wes-2-547-2017, https://doi.org/10.5194/wes-2-547-2017, 2017
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This paper presents an alternative method to evaluate power performance and loads on wind turbines using a blade-mounted flow sensor. A high correlation is found between the wind speed measured at the blades and the power/loads, and simulations indicate that it is possible to reduce the time required for power and load assessment considerably. This result, however, cannot be confirmed from the full-scale measurement study due to practical circumstances.
Related subject area
Thematic area: Dynamics and control | Topic: Dynamics and aeroservoelasticity
Investigating the interactions between wakes and floating wind turbines using FAST.Farm
Uncertainty quantification of structural blade parameters for the aeroelastic damping of wind turbines: a code-to-code comparison
The rotor as a sensor – observing shear and veer from the operational data of a large wind turbine
Experimental validation of a short-term damping estimation method for wind turbines in nonstationary operating conditions
A digital twin solution for floating offshore wind turbines validated using a full-scale prototype
Extreme coherent gusts with direction change – probabilistic model, yaw control, and wind turbine loads
A correction method for large deflections of cantilever beams with a modal approach
A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines
Lucas Carmo, Jason Jonkman, and Regis Thedin
Wind Energ. Sci., 9, 1827–1847, https://doi.org/10.5194/wes-9-1827-2024, https://doi.org/10.5194/wes-9-1827-2024, 2024
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As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.
Hendrik Verdonck, Oliver Hach, Jelmer D. Polman, Otto Schramm, Claudio Balzani, Sarah Müller, and Johannes Rieke
Wind Energ. Sci., 9, 1747–1763, https://doi.org/10.5194/wes-9-1747-2024, https://doi.org/10.5194/wes-9-1747-2024, 2024
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Aeroelastic stability simulations are needed to guarantee the safety and overall robust design of wind turbines. To increase our confidence in these simulations in the future, the sensitivity of the stability analysis with respect to variability in the structural properties of the wind turbine blades is investigated. Multiple state-of-the-art tools are compared and the study shows that even though the tools predict similar stability behavior, the sensitivity might be significantly different.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
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A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Kristian Ladefoged Ebbehøj, Philippe Jacques Couturier, Lars Morten Sørensen, and Jon Juel Thomsen
Wind Energ. Sci., 9, 1005–1024, https://doi.org/10.5194/wes-9-1005-2024, https://doi.org/10.5194/wes-9-1005-2024, 2024
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This paper experimentally validates a novel method for characterizing wind turbine dynamics based on vibration measurements. The dynamics of wind turbines can change over short time periods if the operational conditions change. In such cases, conventional methods are inadequate. The validation is performed with a controlled laboratory experiment and a full-scale wind turbine test. More accurate characterization could lead to more efficient wind turbine designs and in turn cheaper wind energy.
Emmanuel Branlard, Jason Jonkman, Cameron Brown, and Jiatian Zhang
Wind Energ. Sci., 9, 1–24, https://doi.org/10.5194/wes-9-1-2024, https://doi.org/10.5194/wes-9-1-2024, 2024
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In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.
Ásta Hannesdóttir, David R. Verelst, and Albert M. Urbán
Wind Energ. Sci., 8, 231–245, https://doi.org/10.5194/wes-8-231-2023, https://doi.org/10.5194/wes-8-231-2023, 2023
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In this work we use observations of large coherent fluctuations to define a probabilistic gust model. The gust model provides the joint description of the gust rise time, amplitude, and direction change. We perform load simulations with a coherent gust according to the wind turbine safety standard and with the probabilistic gust model. A comparison of the simulated loads shows that the loads from the probabilistic gust model can be significantly higher due to variability in the gust parameters.
Ozan Gözcü, Emre Barlas, and Suguang Dou
Wind Energ. Sci., 8, 109–124, https://doi.org/10.5194/wes-8-109-2023, https://doi.org/10.5194/wes-8-109-2023, 2023
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This study proposes a fast correction method for modal-based reduced-order models to account for geometric nonlinearities linked to large bending deflections in cantilever beam-like engineering structures. The large deflections cause secondary motions such as axial and torsional motions when the structures go through bending deflections. The method relies on pre-computed correction terms and thus adds negligibly small extra computational cost to the time domain analyses of the dynamic response.
Emmanuel Branlard and Jens Geisler
Wind Energ. Sci., 7, 2351–2371, https://doi.org/10.5194/wes-7-2351-2022, https://doi.org/10.5194/wes-7-2351-2022, 2022
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The article presents a framework to obtain the linear and nonlinear equations of motion of a multibody system including rigid and flexible bodies. The method yields compact symbolic equations of motion. The applications are many, such as time-domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins.
Cited articles
Alcayaga, L., Larsen, G. C., Kelly, M., and Mann, J.: Large-Scale Coherent
Turbulence Structures in the Atmospheric Boundary Layer over Flat Terrain,
J. Atmos. Sci., 79, 3219–3243, 2022. a
Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J.,
Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., and Sorensen, D.:
LAPACK Users' Guide, third edn., Society for Industrial and Applied Mathematics, Philadelphia, PA, ISBN 978-0-89871-447-0, 1999. a
Becker, M., Ritter, B., Doekemeijer, B., van der Hoek, D., Konigorski, U., Allaerts, D., and van Wingerden, J.-W.: The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake, Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, 2022. a, b
Boersma, S., Doekemeijer, B., Vali, M., Meyers, J., and van Wingerden, J.-W.: A control-oriented dynamic wind farm model: WFSim, Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, 2018. a
Bossanyi, E., Ruisi, R., Larsen, G. C., and Pedersen, M. M.: Axial induction
control design for a field test at Lillgrund wind farm, J. Phys. Conf. Ser., 2265, 042032, https://doi.org/10.1088/1742-6596/2265/4/042032, 2022. a
Branlard, E.: Wind Turbine Aerodynamics and Vorticity-Based Methods, Springer, Cham, https://doi.org/10.1007/978-3-319-55164-7, 2020. a
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, 2014. a
Gebraad, P. M. and Van Wingerden, J.: A control-oriented dynamic model for
wakes in wind plants, J. Phys. Conf. Ser., 524, 012186, https://doi.org/10.1088/1742-6596/524/1/012186, 2014. a
Grunnet, J. D., Soltani, M., Knudsen, T., Kragelund, M. N., and Bak, T.: Aeolustoolbox for dynamics wind farm model, simulation and control, in: The European Wind Energy Conference & Exhibition, EWEC 2010, 20–23 April 2010, Warszawa, Poland, https://vbn.aau.dk/en/publications/aeolus-toolbox-for-dynamics-wind-farm-model-simulationand (last access: 6 September 2023), 2010. a
Hodgson, E., Andersen, S., Troldborg, N., Forsting, A. M., Mikkelsen, R., and
Sørensen, J.: A Quantitative Comparison of Aeroelastic Computations using
Flex5 and Actuator Methods in LES, J. Phys. Conf. Ser., 1934, 012014, https://doi.org/10.1088/1742-6596/1934/1/012014, 2021. a
Horcas, S., Barlas, T., Zahle, F., and Sørensen, N.: Vortex induced
vibrations of wind turbine blades: Influence of the tip geometry, Phys. Fluids, 32, 065104, https://doi.org/10.1063/5.0004005, 2020. a
Jonkman, J., Doubrawa, P., Hamilton, N., Annoni, J., and Fleming, P.:
Validation of FAST.Farm against large-eddy simulations, J. Phys. Conf. Ser., 1037, 062005, https://doi.org/10.1088/1742-6596/1037/6/062005, 2018. a
Keck, R.-E., Veldkamp, D., Madsen, H. A., and Larsen, G.: Implementation of a
mixing length turbulence formulation into the dynamic wake meandering model,
J. Solar Energ. Eng., 134, 021012, https://doi.org/10.1115/1.4006038, 2012. a, b
Keck, R.-E., De Maré, M., Churchfield, M. J., Lee, S., Larsen, G., and
Madsen, H. A.: Two improvements to the dynamic wake meandering model:
including the effects of atmospheric shear on wake turbulence and
incorporating turbulence build-up in a row of wind turbines, Wind Energy, 18,
111–132, https://doi.org/10.1002/we.1686, 2015. a, b
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
Larsen, T. J., Madsen, H. A., Larsen, G. C., and Hansen, K. S.: Validation of
the dynamic wake meander model for loads and power production in the Egmond
aan Zee wind farm, Wind Energy, 16, 605–624, 2013. a
Larsen, T. J., Larsen, G. C., Aagaard Madsen, H., and Petersen, S. M.: Wake
effects above rated wind speed. An overlooked contributor to high loads in
wind farms, in: Scientific Proceedings, EWEA Annual Conference and Exhibition, 17–20 November 2015, Paris, France, 95–99, 2015. a
Lejeune, M., Moens, M., and Chatelain, P.: Extension and validation of an
operational dynamic wake model to yawed configurations, J. Phys. Conf. Ser., 2265, 022018, https://doi.org/10.1088/1742-6596/2265/2/022018, 2022a. a
Lejeune, M., Moens, M., and Chatelain, P.: A meandering-capturing wake model
coupled to rotor-based flow-sensing for operational wind farm flow
prediction, Front. Energ. Res., 10, 884068, https://doi.org/10.3389/fenrg.2022.884068, 2022b. a, b
Liew, J.: HAWC2Farm, Zenodo [code], https://doi.org/10.5281/zenodo.8028485, 2023a. a
Liew, J.: jDWM, Zenodo [code], https://doi.org/10.5281/zenodo.8028555, 2023b. a
Liew, J. and Larsen, G. C.: How does the quantity, resolution, and scaling of
turbulence boxes affect aeroelastic simulation convergence?, J. Phys. Conf. Ser., 2265, 032049, https://doi.org/10.1088/1742-6596/2265/3/032049, 2022. a, b
Liew, J., Andersen, S. J., Troldborg, N., and Göçmen, T.: LES
verification of HAWC2Farm aeroelastic wind farm simulations with wake
steering and load analysis, J. Phys. Conf. Ser., 2265, 022069, https://doi.org/10.1088/1742-6596/2265/2/022069, 2022. a, b, c
Lio, W. H., Larsen, G. C., and Thorsen, G. R.: Dynamic wake tracking using a
cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale
validation, Renew Energ., 172, 1073–1086, 2021. a
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, b
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, 1994. a
Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J.: The aerodynamics of the curled wake: a simplified model in view of flow control, Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, 2019. a
NREL: FLORIS. Version 2.4, Zenodo [code], https://doi.org/10.1115/1.4002555, 2021. a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and
Réthoré, P.-E.: DTUWindEnergy/PyWake: PyWake, Zenodo [code], https://doi.org/10.5281/zenodo.6806136, 2019. a
Ramos-García, N., Sessarego, M., and Horcas, S. G.:
Aero-hydro-servo-elastic coupling of a multi-body finite-element solver and a
multi-fidelity vortex method, Wind Energy, 24, 481–501, 2021. a
Reinwardt, I.: Validierung und Verbesserung von Nachlaufmodellen zur
standortspezifischen Last-und Leistungsberechnung in Windparks, PhD thesis,
Universitätsbibliothek der HSU/UniBwH, https://doi.org/10.24405/14145, 2022. a
Reinwardt, I., Gerke, N., Dalhoff, P., Steudel, D., and Moser, W.: Validation
of wind turbine wake models with focus on the dynamic wake meandering model,
J. Phys. Conf. Ser., 1037, 072028,https://doi.org/10.1088/1742-6596/1037/7/072028, 2018. a
Riva, R., Liew, J., Friis-Møller, M., Dimitrov, N., Barlas, E., Réthoré,
P.-E., and Beržonskis, A.: Wind farm layout optimization with load
constraints using surrogate modelling, J. Phys. Conf. Ser.,
1618, 042035, https://doi.org/10.1088/1742-6596/1618/4/042035, 2020. a
Sood, I., Simon, E., Vitsas, A., Blockmans, B., Larsen, G. C., and Meyers, J.: Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm, Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, 2022. a, b
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. Roy. Soc. A, 373, 20140071, https://doi.org/10.1098/rsta.2014.0071, 2015. a
Stieren, A., Gadde, S. N., and Stevens, R. J.: Modeling dynamic wind direction
changes in large eddy simulations of wind farms, Renew. Energ., 170,
1342–1352, 2021. a
Technical University of Denmark: Sophia HPC Cluster,
https://doi.org/10.57940/FAFC-6M81, 2019. a, b, c
Vasiljević, N., Lea, G., Courtney, M., Cariou, J.-P., Mann, J., and
Mikkelsen, T.: Long-range WindScanner system, Remote Sens.-Basel, 8, 896, https://doi.org/10.3390/rs8110896, 2016. a
Short summary
We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
We present recent research on dynamically modelling wind farm wakes and integrating these...
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