Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-309-2020
© Author(s) 2020. 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-5-309-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Mar 2020
Research article |
| 05 Mar 2020
| 05 Mar 2020
Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations
Paul Hulsman
ForWind – University of Oldenburg, Research Group Wind Energy Systems, Küpkersweg 70, 26129 Oldenburg, Germany
Wind Energy Department, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Lyngby, Denmark
Tuhfe Göçmen
Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Wind Energ. Sci., 9, 1483–1505, https://doi.org/10.5194/wes-9-1483-2024, https://doi.org/10.5194/wes-9-1483-2024, 2024
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We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
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As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn
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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, Gerald Steinfeld, Paul Hulsman, and Martin Kühn
Wind Energ. Sci., 8, 747–770, https://doi.org/10.5194/wes-8-747-2023, https://doi.org/10.5194/wes-8-747-2023, 2023
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The optimal misalignment angles for wake steering are determined using wake models. Although mostly analytical, data-driven models have recently shown promising results. This study validates a previously proposed data-driven model with results from a field experiment using lidar measurements. In a comparison with a state-of-the-art analytical model, it shows systematically more accurate estimates of the available power. Also when using only commonly available input data, it gives good results.
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.
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-96, https://doi.org/10.5194/wes-2024-96, 2024
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The use of a global base in a proper orthogonal decomposition provides a common base for analyzing flows, such as wind turbine wakes, across an entire parameter space. This can be used to compare flows with different conditions using the same physical interpretation. This work shows the convergence of the global base, its small error compared to the truncation error of 100 modes in the proper orthogonal decomposition, and the insensitivity to which datasets are included for generating it.
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We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
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As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn
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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.
Balthazar Arnoldus Maria Sengers, Gerald Steinfeld, Paul Hulsman, and Martin Kühn
Wind Energ. Sci., 8, 747–770, https://doi.org/10.5194/wes-8-747-2023, https://doi.org/10.5194/wes-8-747-2023, 2023
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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.
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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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.
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.
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.
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, 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.
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.
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.
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.
Related subject area
Wind and turbulence
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Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models
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Investigation into boundary layer transition using wall-resolved large-eddy simulations and modeled inflow turbulence
Evaluation of the global-blockage effect on power performance through simulations and measurements
Development of an automatic thresholding method for wake meandering studies and its application to the data set from scanning wind lidar
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RANS modeling of a single wind turbine wake in the unstable surface layer
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Validation of wind resource and energy production simulations for small wind turbines in the United States
Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars
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Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain
Validation of a coupled atmospheric–aeroelastic model system for wind turbine power and load calculations
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The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea
On turbulence models and lidar measurements for wind turbine control
Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data
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Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling
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Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations
Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics
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A pressure-driven atmospheric boundary layer model satisfying Rossby and Reynolds number similarity
Design and analysis of a wake model for spatially heterogeneous flow
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Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast
Wind turbines in atmospheric flow: fluid–structure interaction simulations with hybrid turbulence modeling
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Understanding and mitigating the impact of data gaps on offshore wind resource estimates
Investigating the loads and performance of a model horizontal axis wind turbine under reproducible IEC extreme operational conditions
Validation of the dynamic wake meandering model with respect to loads and power production
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Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy
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Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, https://doi.org/10.5194/wes-7-1153-2022, 2022
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Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
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.
Brandon Arthur Lobo, Alois Peter Schaffarczyk, and Michael Breuer
Wind Energ. Sci., 7, 967–990, https://doi.org/10.5194/wes-7-967-2022, https://doi.org/10.5194/wes-7-967-2022, 2022
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This research involves studying the flow around the section of a wind turbine blade, albeit at a lower Reynolds number or flow speed, using wall-resolved large-eddy simulations, a form of computer simulation that resolves the important scales of the flow. Among the many interesting results, it is shown that the energy entering the boundary layer around the airfoil or section of the blade is proportional to the square of the incoming flow turbulence intensity.
Alessandro Sebastiani, Alfredo Peña, Niels Troldborg, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 875–886, https://doi.org/10.5194/wes-7-875-2022, https://doi.org/10.5194/wes-7-875-2022, 2022
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The power performance of a wind turbine is often tested with the turbine standing in a row of several wind turbines, as it is assumed that the performance is not affected by the neighbouring turbines. We test this assumption with both simulations and measurements, and we show that the power performance can be either enhanced or lowered by the neighbouring wind turbines. Consequently, we also show how power performance testing might be biased when performed on a row of several wind turbines.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Wind Energ. Sci., 7, 849–873, https://doi.org/10.5194/wes-7-849-2022, https://doi.org/10.5194/wes-7-849-2022, 2022
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We described a new automated method to separate the wind turbine wake from the undisturbed flow. The method relies on the wind speed distribution in the measured wind field to select one specific threshold value and split the measurements into wake and background points. The purpose of the method is to reduce the amount of data required – the proposed algorithm does not need precise information on the wind speed or direction and can run on the image instead of the measured data.
Wei Fu, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 7, 831–848, https://doi.org/10.5194/wes-7-831-2022, https://doi.org/10.5194/wes-7-831-2022, 2022
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Measuring the variability of the wind is essential to operate the wind turbines safely. Lidars of different configurations have been placed on the turbines’ nacelle to measure the inflow remotely. This work found that the multiple-beam lidar is the only one out of the three employed nacelle lidars that can give detailed information about the inflow variability. The other two commercial lidars, which have two and four beams, respectively, measure only the fluctuation in the along-wind direction.
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.
Oliver Maas and Siegfried Raasch
Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, https://doi.org/10.5194/wes-7-715-2022, 2022
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In the future there will be very large wind farm clusters in the German Bight. This study investigates how the wind field is affected by these very large wind farms and how much energy can be extracted by the wind turbines. Very large wind farms do not only reduce the wind speed but can also cause a change in wind direction or temperature. The extractable energy per wind turbine is much smaller for large wind farms than for small wind farms due to the reduced wind speed inside the wind farms.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
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The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
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Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
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In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Tobias Klaas-Witt and Stefan Emeis
Wind Energ. Sci., 7, 413–431, https://doi.org/10.5194/wes-7-413-2022, https://doi.org/10.5194/wes-7-413-2022, 2022
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Light detection and ranging (lidar) has become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, because of their measurement principle, common lidars suffer from errors at orographically complex, i.e. hilly or mountainous, sites. This study analyses the impact of the five main influencing factors in a non-dimensional, model-based parameter study.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
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Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Sonja Krüger, Gerald Steinfeld, Martin Kraft, and Laura J. Lukassen
Wind Energ. Sci., 7, 323–344, https://doi.org/10.5194/wes-7-323-2022, https://doi.org/10.5194/wes-7-323-2022, 2022
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Detailed numerical simulations of turbines in atmospheric conditions are challenging with regard to their computational demand. We coupled an atmospheric flow model and a turbine model in order to deliver extensive details about the flow and the turbine response within reasonable computational time. A comparison to measurement data was performed and showed a very good agreement. The efficiency of the tool enables applications such as load calculation in wind farms or during low-level-jet events.
Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri
Wind Energ. Sci., 7, 345–365, https://doi.org/10.5194/wes-7-345-2022, https://doi.org/10.5194/wes-7-345-2022, 2022
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Wake steering control, in which turbines are intentionally misaligned with the incident wind, has demonstrated potential to increase wind farm energy. We investigate wake steering control methods in simulations of a wind farm operating in the terrestrial diurnal cycle. We develop a statistical wind direction forecast to improve wake steering in flows with time-varying states. Closed-loop wake steering control increases wind farm energy production, compared to baseline and open-loop control.
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.
Ingrid Neunaber, Joachim Peinke, and Martin Obligado
Wind Energ. Sci., 7, 201–219, https://doi.org/10.5194/wes-7-201-2022, https://doi.org/10.5194/wes-7-201-2022, 2022
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Wind turbines are often clustered within wind farms. A consequence is that some wind turbines may be exposed to the wakes of other turbines, which reduces their lifetime due to the wake turbulence. Knowledge of the wake is thus important, and we carried out wind tunnel experiments to investigate the wakes. We show how models that describe wakes of bluff bodies can help to improve the understanding of wind turbine wakes and wind turbine wake models, particularly by including a virtual origin.
Elena Cantero, Javier Sanz, Fernando Borbón, Daniel Paredes, and Almudena García
Wind Energ. Sci., 7, 221–235, https://doi.org/10.5194/wes-7-221-2022, https://doi.org/10.5194/wes-7-221-2022, 2022
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The impact of atmospheric stability on wind energy is widely demonstrated, so we have to know how to characterise it.
This work based on a meteorological mast located in a complex terrain compares and evaluates different instrument set-ups and methodologies for stability characterisation. The methods are examined considering their theoretical background, implementation complexity, instrumentation requirements and practical use in connection with wind energy applications.
Peter Brugger, Corey Markfort, and Fernando Porté-Agel
Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, https://doi.org/10.5194/wes-7-185-2022, 2022
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Wind turbines create a wake of reduced wind speeds downstream of the rotor. The wake does not necessarily have a straight, pencil-like shape but can meander similar to a smoke plume. We investigated this wake meandering and observed that the downstream transport velocity is slower than the wind speed contrary to previous assumptions and that the evolution of the atmospheric turbulence over time impacts wake meandering on distances typical for the turbine spacing in wind farms.
Ida Marie Solbrekke, Asgeir Sorteberg, and Hilde Haakenstad
Wind Energ. Sci., 6, 1501–1519, https://doi.org/10.5194/wes-6-1501-2021, https://doi.org/10.5194/wes-6-1501-2021, 2021
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We validate new high-resolution data set (NORA3) for offshore wind power purposes for the North Sea and the Norwegian Sea. The aim of the validation is to ensure that NORA3 can act as a wind resource data set in the planning phase for future offshore wind power installations in the area of concern. The general conclusion of the validation is that NORA3 is well suited for wind power estimates but gives slightly conservative estimates of the offshore wind metrics.
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.
Alexander Basse, Doron Callies, Anselm Grötzner, and Lukas Pauscher
Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, https://doi.org/10.5194/wes-6-1473-2021, 2021
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This study investigates systematic, seasonal biases in the long-term correction of short-term wind measurements (< 1 year). Two popular measure–correlate–predict (MCP) methods yield remarkably different results. Six reanalysis data sets serve as long-term data. Besides experimental results, theoretical findings are presented which link the mechanics of the methods and the properties of the reanalysis data sets to the observations. Finally, recommendations for wind park planners are derived.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
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This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Rogier Floors, Merete Badger, Ib Troen, Kenneth Grogan, and Finn-Hendrik Permien
Wind Energ. Sci., 6, 1379–1400, https://doi.org/10.5194/wes-6-1379-2021, https://doi.org/10.5194/wes-6-1379-2021, 2021
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Wind turbines are frequently placed in forests. We investigate the potential of using satellites to characterize the land surface for wind flow modelling. Maps of forest properties are generated from satellite data and converted to flow modelling maps. Validation is carried out at 10 sites. Using the novel satellite-based maps leads to lower errors of the power density than land cover databases, which demonstrates the value of using satellite-based land cover maps for flow modelling.
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci., 6, 1205–1226, https://doi.org/10.5194/wes-6-1205-2021, https://doi.org/10.5194/wes-6-1205-2021, 2021
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As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
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.
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci., 6, 1117–1142, https://doi.org/10.5194/wes-6-1117-2021, https://doi.org/10.5194/wes-6-1117-2021, 2021
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We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
Tanvi Gupta and Somnath Baidya Roy
Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, https://doi.org/10.5194/wes-6-1089-2021, 2021
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Wind turbines extract momentum from atmospheric flow and convert that to electricity. This study explores recovery processes in wind farms that replenish the momentum so that wind farms can continue to function. Experiments with a numerical model show that momentum transport by turbulent eddies from above the wind turbines is the major contributor to recovery except for strong wind conditions and low wind turbine density, where horizontal advection can also play a major role.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, https://doi.org/10.5194/wes-6-1043-2021, 2021
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As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of data of two floating lidars to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, https://doi.org/10.5194/wes-6-1015-2021, 2021
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Low-level jets (LLJs) are pronounced maxima in wind speed profiles affecting wind turbine performance and longevity. We present a climatology of LLJs over Iowa using output from the Weather Research and Forecasting (WRF) model and determine the rotor plane conditions when they occur. LLJ characteristics are highly sensitive to the identification criteria applied, and different (unique) LLJs are extracted with each criterion. LLJ characteristics also vary with different model output resolution.
Janna Kristina Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, https://doi.org/10.5194/wes-6-997-2021, 2021
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Fluctuations in the power output of wind turbines are one of the major challenges in the integration and utilisation of wind energy. By analysing the power output fluctuations of wind turbine pairs in an offshore wind farm, we show that their correlation depends on their location within the wind farm and their inflow. The main outcome is that these correlation dependencies can be characterised by statistics of the power output of the wind turbines and sorted by a clustering algorithm.
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, https://doi.org/10.5194/wes-6-935-2021, 2021
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Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
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We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
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.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Carlo Cossu
Wind Energ. Sci., 6, 663–675, https://doi.org/10.5194/wes-6-663-2021, https://doi.org/10.5194/wes-6-663-2021, 2021
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We deal with wake redirection, which is a promising approach designed to mitigate turbine–wake interactions which have a negative impact on the performance and lifetime of wind farms. We show that substantial power gains can be obtained by tilting the rotors of spanwise-periodic wind-turbine arrays in the atmospheric boundary layer (ABL). Optimal relative rotor sizes and spanwise spacings exist, which maximize the global power extracted from the wind.
Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, https://doi.org/10.5194/wes-6-645-2021, 2021
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We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
Christian Grinderslev, Niels Nørmark Sørensen, Sergio González Horcas, Niels Troldborg, and Frederik Zahle
Wind Energ. Sci., 6, 627–643, https://doi.org/10.5194/wes-6-627-2021, https://doi.org/10.5194/wes-6-627-2021, 2021
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This study investigates aero-elasticity of wind turbines present in the turbulent and chaotic wind flow of the lower atmosphere, using fluid–structure interaction simulations. This method combines structural response computations with high-fidelity modeling of the turbulent wind flow, using a novel turbulence model which combines the capabilities of large-eddy simulations for atmospheric flows with improved delayed detached eddy simulations for the separated flow near the rotor.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
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A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Kamran Shirzadeh, Horia Hangan, Curran Crawford, and Pooyan Hashemi Tari
Wind Energ. Sci., 6, 477–489, https://doi.org/10.5194/wes-6-477-2021, https://doi.org/10.5194/wes-6-477-2021, 2021
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Wind energy systems work coherently in atmospheric flows which are gusty. This causes highly variable power productions and high fatigue loads on the system, which together hold back further growth of the wind energy market. This study demonstrates an alternative experimental procedure to investigate some extreme wind condition effects on wind turbines based on the IEC standard. This experiment can be improved upon and used to develop new control concepts, mitigating the effect of gusts.
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci., 6, 441–460, https://doi.org/10.5194/wes-6-441-2021, https://doi.org/10.5194/wes-6-441-2021, 2021
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This analysis validates the DWM model based on loads and power production measured at an onshore wind farm. Special focus is given to the performance of a version of the DWM model that was previously recalibrated with a lidar system at the site. The results of the recalibrated wake model agree very well with the measurements. Furthermore, lidar measurements of the wind speed deficit and the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.
Christian Ingenhorst, Georg Jacobs, Laura Stößel, Ralf Schelenz, and Björn Juretzki
Wind Energ. Sci., 6, 427–440, https://doi.org/10.5194/wes-6-427-2021, https://doi.org/10.5194/wes-6-427-2021, 2021
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Wind farm sites in complex terrain are subject to local wind phenomena, which are difficult to quantify but have a huge impact on a wind turbine's annual energy production. Therefore, a wind sensor was applied on an unmanned aerial vehicle and validated against stationary wind sensors with good agreement. A measurement over complex terrain showed local deviations from the mean wind speed of approx. ± 30 %, indicating the importance of an extensive site evaluation to reduce investment risk.
Ervin Bossanyi and Renzo Ruisi
Wind Energ. Sci., 6, 389–408, https://doi.org/10.5194/wes-6-389-2021, https://doi.org/10.5194/wes-6-389-2021, 2021
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This paper describes the design and field testing of a controller for reducing wake interactions on a wind farm. Reducing the power of some turbines weakens their wakes, allowing other turbines to produce more power so that the total wind farm power may increase. There have been doubts that this is feasible, but these field tests on a full-scale wind farm indicate that this goal has been achieved, also providing convincing validation of the model used for designing the controller.
Carlo Cossu
Wind Energ. Sci., 6, 377–388, https://doi.org/10.5194/wes-6-377-2021, https://doi.org/10.5194/wes-6-377-2021, 2021
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In this study wake redirection and axial-induction control are combined to mitigate turbine–wake interactions, which have a negative impact on the performance and lifetime of wind farms. The results confirm that substantial power gains are obtained when overinduction is combined with tilt control. More importantly, the approach is extended to the case of yaw control, showing that large power gain enhancements are obtained by means of static overinductive yaw control.
Joseph C. Y. Lee and M. Jason Fields
Wind Energ. Sci., 6, 311–365, https://doi.org/10.5194/wes-6-311-2021, https://doi.org/10.5194/wes-6-311-2021, 2021
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This review paper evaluates the energy prediction bias in the wind resource assessment process, and the overprediction bias is decreasing over time. We examine the estimated and observed losses and uncertainties in energy production from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 standard. The considerable uncertainties call for further improvements in the prediction methodologies and more observations for validation.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
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Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
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.
Cited articles
Anderson, M.: Horizontal axis wind turbines in yaw, in: Wind Energy Workshop,
57–67, Multi-Science Publishing, Proceedings of the First BWEA Wind Energy Workshop, 1979. 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, b, c
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, 2018a. a
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, 2018b. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine
wakes, Renew. Energ., 70, 116–123, 2014. a
Branlard, E: Wind turbine aerodynamics and vorticity-based methods, Vol. 10, New York, Springer, 2017. a
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energ. Sci., 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018. a
Fleming, P., Annoni, J., Churchfield, M., Martinez-Tossas, L. A., Gruchalla, K., Lawson, M., and Moriarty, P.: A simulation study demonstrating the importance of large-scale trailing vortices in wake steering, Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, 2018. a
Fleming, P. 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
Fleming, P. A., Ning, A., Gebraad, P. M., and Dykes, K.: Wind plant system
engineering through optimization of layout and yaw control, Wind Energy, 19,
329–344, 2016. a
Gaumond, M., Réthoré, P. E., Ott, S., Peña, A., Bechmann, A., and
Hansen, K. S.: Evaluation of the wind direction uncertainty and its impact
on wake modeling at the Horns Rev offshore wind farm, Wind Energy, 17, 1169–1178,
https://doi.org/10.1002/we.1625, 2014. a
Gebraad, P., Teeuwisse, F., Wingerden, J., Fleming, P. A., Ruben, S., Marden,
J., and Pao, L.: Wind plant power optimization through yaw control using a
parametric model for wake effects – a CFD simulation study, Wind Energy, 19,
95–114, 2016. 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
Gilling, L., Sørensen, N. N., and Réthoré, P.-E.: Imposing resolved
turbulence by an actuator in a detached eddy simulation of an airfoil, EWEC, European Wind Energy Conference and Exhibition – Marseille, France, 2009. a
Hasager, C., Nygaard, N., Volker, P., Karagali, I., Andersen, S., and Badger,
J.: Wind Farm Wake: The 2016 Horns Rev Photo Case, Energies, 10, 317,
https://doi.org/10.3390/en10030317, 2017. a
Hulsman, P.: PCE surrogates for power and loads under wind farm control, available at: https://github.com/Paul1994H/Surrogates-Wind-Farm-Control-Model.git, last access: 13 November 2019. a
Jiménez, Á., 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, 2010. 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
Knudsen, T., Bak, T., and Svenstrup, M.: Survey of wind farm control–power
and fatigue optimization, Wind Energy, 18, 1333–1351, 2015. a
Kragh, K. A. and Hansen, M. H.: Potential of power gain with improved yaw
alignment, Wind Energy, 18, 979–989, 2015. a
Leonard, B. P.: A stable and accurate convective modelling procedure based on
quadratic upstream interpolation, Comput. Method. Appl. M., 19, 59–98, 1979. 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. Discuss., https://doi.org/10.5194/wes-2019-65, in review, 2019. a
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, 1994. a
Mann, J.: Wind field simulation, Probabilist. Eng. Mech., 13,
269–282, 1998. a
Mann, J., Peña, A., Troldborg, N., and Andersen, S. J.: How does turbulence change approaching a rotor?, Wind Energ. Sci., 3, 293–300, https://doi.org/10.5194/wes-3-293-2018, 2018. 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
McKay, P., Carriveau, R., and Ting, D. S.-K.: Wake impacts on downstream wind
turbine performance and yaw alignment, Wind Energy, 16, 221–234,
https://doi.org/10.1002/we.544, 2013. a
Medici, D. and Alfredsson, P.: Measurements on a wind turbine wake: 3D effects
and bluff body vortex shedding, Wind Energy, 9, 219–236, 2006. a
Medici, D. and Dahlberg, J.: Potential improvement of wind turbine array
efficiency by active wake control (AWC), Proceedings of European Wind Energy Conference, Madrid, 2003. a
Michelsen, J. A.: Basis3D-a platform for development of multiblock PDE solvers,
Tech. rep., Technical Report AFM 92-05, PhD thesis, Technical University of Denmark, Lyngby,
1992. a
Mikkelsen, R.: Actuator disc methods applied to wind turbines, PhD thesis, Technical
University of Denmark, Lyngby, 2003. a
Mittelmeier, N. and Kühn, M.: Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data, Wind Energ. Sci., 3, 395–408, https://doi.org/10.5194/wes-3-395-2018, 2018. a
Murcia, J. P., Réthoré, P.-E., Dimitrov, N., Natarajan, A.,
Sørensen, J. D., Graf, P., and Kim, T.: Uncertainty propagation through an
aeroelastic wind turbine model using polynomial surrogates, Renew. Energ.,
119, 910–922, 2018. 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), Technical University of Denmark, Lyngby, 1996. a
Quick, J., Annoni, J., King, R., Dykes, K., Fleming, P., and Ning, A.:
Optimization Under Uncertainty for Wake Steering Strategies, J.
Phys. Conf. Ser., 854, 012036,
https://doi.org/10.1088/1742-6596/854/1/012036, 2017.
a
Ribner, H. S.: Propellers in yaw, NASA, Advance Restricted Report 3L09, NACA Wartime Report L-219, Langley Field, VA, USA, 1943. a
Smulders, P. T., Lenssen, G., and van Leeuwen, H.: Experiments with windrotors
in yaw, Technische Hogeschool, Afdeling der Technische Natuurkunde, Vakgroep
Transportfysica, Proceedings of the International Symposium on Envi-
ronmental Problems, Patras, Greece, 1981. a
Sørensen, J. N. and Shen, W. Z.: Numerical modeling of wind turbine wakes,
J. Fluids Eng., 124, 393–399, 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
Sørensen, N. N.: General purpose flow solver applied to flow over hills,
Risø National Laboratory, Risø, 1995. a
Sudret, B.: Global sensitivity analysis using polynomial chaos expansions,
Reliab. Eng. Syst. Safe., 93, 964–979, 2008. a
Troldborg, N.: Actuator line modeling of wind turbine wakes, PhD thesis, Technical University of Denmark, Lyngby, 2009. a
Troldborg, N. and Meyer Forsting, A. R.: A simple model of the wind turbine
induction zone derived from numerical simulations, Wind Energy, 20,
2011–2020, 2017. a
Troldborg, N., Sørensen, J. N., Mikkelsen, R., and Sørensen, N. N.: A
simple atmospheric boundary layer model applied to large eddy simulations of
wind turbine wakes, Wind Energy, 17, 657–669, 2014. a
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., Li, Y., and Rotea, M. A.:
Yaw-Misalignment and its Impact on Wind Turbine Loads and Wind Farm Power
Output, J. Phys. Conf. Ser., 753, 062013, https://doi.org/10.1088/1742-6596/753/6/062013, 2016. a
Ziegler, L., Gonzalez, E., Rubert, T., Smolka, U., and Melero, J. J.: Lifetime
extension of onshore wind turbines: A review covering Germany, Spain,
Denmark, and the UK, Renew. Sust. Energ. Rev., 82, 1261–1271, https://doi.org/10.1016/j.rser.2017.09.100, 2018. a
Short summary
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The...
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