Articles | Volume 6, issue 1
https://doi.org/10.5194/wes-6-159-2021
© Author(s) 2021. 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-6-159-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jan 2021
Research article |
| 27 Jan 2021
| 27 Jan 2021
Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy
Bart M. Doekemeijer
CORRESPONDING AUTHOR
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Stefan Kern
GE Renewable Energy, 85748 Garching, Germany
Sivateja Maturu
GE Renewable Energy, 85748 Garching, Germany
Wind Energy Institute, Technische Universität München, 85748 Garching, Germany
Stoyan Kanev
TNO Energy Transition, Westerduinweg 3, 1755 LE Petten, the Netherlands
Bastian Salbert
Wind Energy Institute, Technische Universität München, 85748 Garching, Germany
Johannes Schreiber
Wind Energy Institute, Technische Universität München, 85748 Garching, Germany
Filippo Campagnolo
Wind Energy Institute, Technische Universität München, 85748 Garching, Germany
Carlo L. Bottasso
Wind Energy Institute, Technische Universität München, 85748 Garching, Germany
Simone Schuler
GE Renewable Energy, 85748 Garching, Germany
Friedrich Wilts
UL International GmbH – DEWI, Ebertstrasse 96, 26382 Wilhelmshaven, Germany
Thomas Neumann
UL International GmbH – DEWI, Ebertstrasse 96, 26382 Wilhelmshaven, Germany
Giancarlo Potenza
Enel Green Power, Viale Regina Margherita 125, Rome, Italy
Fabio Calabretta
Enel Green Power, Viale Regina Margherita 125, Rome, Italy
Federico Fioretti
Enel Green Power, Viale Regina Margherita 125, Rome, Italy
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
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47 citations as recorded by crossref.
- Vertical wake deflection for floating wind turbines by differential ballast control E. Nanos et al. 10.5194/wes-7-1641-2022
- An open-source framework for the development, deployment and testing of wind farm control strategies C. Sucameli et al. 10.1088/1742-6596/2767/9/092043
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- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
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- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Reconstruction of environmental site conditions by the integration of SCADA and reanalysis data A. Vad & C. Bottasso 10.1088/1742-6596/2767/9/092073
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- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. 10.1016/j.oceaneng.2023.116004
- Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects M. van den Broek et al. 10.5194/wes-8-1909-2023
- Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al. 10.1088/1742-6596/2767/9/092075
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. 10.5194/wes-9-1507-2024
- Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao 10.1016/j.enconman.2023.117507
- Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. 10.3389/fenrg.2022.883889
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
- Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm M. Baricchio et al. 10.5194/wes-9-2113-2024
- Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
- Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. 10.5194/wes-8-401-2023
- Design and Analysis of Yaw Command Tracking Control Algorithm for Wind Turbine for Wind Farm Control Application T. Jeon et al. 10.7836/kses.2021.41.3.065
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
- Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment D. Houck et al. 10.5194/wes-9-1189-2024
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
- The value of wake steering wind farm flow control in US energy markets E. Simley et al. 10.5194/wes-9-219-2024
- Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al. 10.1016/j.renene.2022.10.120
- Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty Y. Wang et al. 10.1016/j.apenergy.2023.121849
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- Field validation of a yaw misalignment observer for wind farm control M. Bertelè et al. 10.1088/1742-6596/2767/9/092013
- Measuring wake deflection from SCADA data during wake steering using machine learning N. Post et al. 10.1088/1742-6596/2767/4/042031
- A quantitative approach for evaluating fatigue damage under wake effects and yaw control for offshore wind turbines F. Lu et al. 10.1016/j.seta.2024.103824
- Axial induction controller field test at Sedini wind farm E. Bossanyi & R. Ruisi 10.5194/wes-6-389-2021
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. 10.1016/j.oceaneng.2024.117691
- Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant E. Simley et al. 10.1088/1742-6596/2265/3/032097
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- Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry D. van der Hoek et al. 10.1002/we.2896
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Latest update: 14 Nov 2024
Short summary
This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
This article presents the results of a field experiment investigating wake steering on an...
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