Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-1791-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1791-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2022
Research article |
| 08 Sep 2022
| 08 Sep 2022
FarmConners wind farm flow control benchmark – Part 1: Blind test results
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Filippo Campagnolo
Wind Energy Institute, Technische Universität München, 85748 Garching b. München, Germany
Thomas Duc
ENGIE Green, 6 rue Alexander Fleming, 69007 Lyon, France
Irene Eguinoa
Wind Energy Department, CENER, Sarriguren, Spain
Søren Juhl Andersen
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Vlaho Petrović
ForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
Lejla Imširović
Wind Energy Institute, Technische Universität München, 85748 Garching b. München, Germany
Robert Braunbehrens
Wind Energy Institute, Technische Universität München, 85748 Garching b. München, Germany
Jaime Liew
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Mads Baungaard
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Maarten Paul van der Laan
DTU Wind and Energy Systems, Technical University of Denmark, Lyngby/Roskilde, Denmark
Guowei Qian
Department of Civil Engineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan
Maria Aparicio-Sanchez
Wind Energy Department, CENER, Sarriguren, Spain
Rubén González-Lope
Wind Energy Department, CENER, Sarriguren, Spain
Vinit V. Dighe
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Marcus Becker
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Maarten J. van den Broek
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Adam Stock
Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, The University of Strathclyde, Glasgow, UK
Matthew Cole
Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, The University of Strathclyde, Glasgow, UK
Renzo Ruisi
DNV, Group Research & Development, Bristol, United Kingdom
Ervin Bossanyi
DNV, Group Research & Development, Bristol, United Kingdom
Niklas Requate
Fraunhofer IWES, Bremerhaven, Germany
Simon Strnad
Fraunhofer IWES, Bremerhaven, Germany
Jonas Schmidt
Fraunhofer IWES, Bremerhaven, Germany
Lukas Vollmer
Fraunhofer IWES, Bremerhaven, Germany
Ishaan Sood
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, Leuven 3001, Belgium
Johan Meyers
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, Leuven 3001, Belgium
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Cited
10 citations as recorded by crossref.
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- 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
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
- Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling J. Liew et al. 10.1088/1742-6596/2767/9/092039
- Dynamic interaction of inflow and rotor time scales and impact on single turbine wake recovery S. Andersen et al. 10.1088/1742-6596/2767/9/092002
- 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
- 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
- Stochastic wind farm flow generation using a reduced order model of LES S. Andersen & J. Murcia Leon 10.1088/1742-6596/2505/1/012050
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. 10.3389/fenrg.2022.883889
9 citations as recorded by crossref.
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- 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
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
- Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling J. Liew et al. 10.1088/1742-6596/2767/9/092039
- Dynamic interaction of inflow and rotor time scales and impact on single turbine wake recovery S. Andersen et al. 10.1088/1742-6596/2767/9/092002
- 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
- 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
- Stochastic wind farm flow generation using a reduced order model of LES S. Andersen & J. Murcia Leon 10.1088/1742-6596/2505/1/012050
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
1 citations as recorded by crossref.
Latest update: 14 Nov 2024
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control...
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