Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2163-2022
© 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-7-2163-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2022
Research article |
| 01 Nov 2022
| 01 Nov 2022
The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake
Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands
Bastian Ritter
Control Systems and Mechatronics Lab, Technische Universität Darmstadt, Landgraf Georg Str. 4, 64283 Darmstadt, Germany
Bart Doekemeijer
National Renewable Energy Laboratory, Golden, CO 80401, USA
Daan van der Hoek
Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands
Ulrich Konigorski
Control Systems and Mechatronics Lab, Technische Universität Darmstadt, Landgraf Georg Str. 4, 64283 Darmstadt, Germany
Dries Allaerts
Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands
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29 citations as recorded by crossref.
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Dries Allaerts, 1989–2024 M. Bastankhah et al. 10.5194/wes-9-2171-2024
- An advanced farm flow estimator for the real-time evaluation of the potential wind power of a down-regulated wind farm M. Moens et al. 10.1088/1742-6596/2767/3/032044
- Optimal combined wake and active power control of large‐scale wind farm considering available power W. Chen et al. 10.1049/rpg2.12883
- Transient phenomena study in wind energy by means of LES simulations: impact of wind direction changes D. Barile et al. 10.1088/1742-6596/2505/1/012038
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model B. Li et al. 10.1063/5.0223631
- Wake Mixing Control For Floating Wind Farms: Analysis of the Implementation of the Helix Wake Mixing Strategy on the IEA 15-MW Floating Wind Turbine D. van den Berg et al. 10.1109/MCS.2024.3432341
- Dynamic wake conditions tailored by an active grid in the wind tunnel D. Onnen et al. 10.1088/1742-6596/2767/4/042038
- 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
- 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
- LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. 10.1088/1742-6596/2767/3/032041
- Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn M. Becker et al. 10.3390/en15228589
- Study on the yaw-based wake steering control considering dynamic flow characteristics for wind farm power improvement X. Yu et al. 10.1088/1742-6596/2505/1/012010
- Model predictive control of wakes for wind farm power tracking A. Sterle et al. 10.1088/1742-6596/2767/3/032005
- A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
- Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm J. Liew et al. 10.5194/wes-8-1387-2023
- An integrated deep neural network framework for predicting the wake flow in the wind field S. Sun et al. 10.1016/j.energy.2024.130400
- 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
- Dynamic response of a wind turbine wake subjected to surge and heave step motions under different inflow conditions A. Hubert et al. 10.1088/1742-6596/2767/9/092035
- MPC-Based Fatigue Load Suppression of Waked Wind Farm With 2Dof WT Control Strategy W. Chen et al. 10.1109/TSTE.2024.3407775
- Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
- Study of a dynamic effect-based method for wind farm yaw control optimization L. Li et al. 10.1080/15435075.2023.2297771
- The dynamic coupling between the pulse wake mixing strategy and floating wind turbines D. van den Berg et al. 10.5194/wes-8-849-2023
- Development of a dynamic wake model accounting for wake advection delays and mesoscale wind transients B. Foloppe et al. 10.1088/1742-6596/2265/2/022055
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake M. Becker et al. 10.5194/wes-7-2163-2022
- A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
25 citations as recorded by crossref.
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Dries Allaerts, 1989–2024 M. Bastankhah et al. 10.5194/wes-9-2171-2024
- An advanced farm flow estimator for the real-time evaluation of the potential wind power of a down-regulated wind farm M. Moens et al. 10.1088/1742-6596/2767/3/032044
- Optimal combined wake and active power control of large‐scale wind farm considering available power W. Chen et al. 10.1049/rpg2.12883
- Transient phenomena study in wind energy by means of LES simulations: impact of wind direction changes D. Barile et al. 10.1088/1742-6596/2505/1/012038
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model B. Li et al. 10.1063/5.0223631
- Wake Mixing Control For Floating Wind Farms: Analysis of the Implementation of the Helix Wake Mixing Strategy on the IEA 15-MW Floating Wind Turbine D. van den Berg et al. 10.1109/MCS.2024.3432341
- Dynamic wake conditions tailored by an active grid in the wind tunnel D. Onnen et al. 10.1088/1742-6596/2767/4/042038
- 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
- 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
- LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. 10.1088/1742-6596/2767/3/032041
- Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn M. Becker et al. 10.3390/en15228589
- Study on the yaw-based wake steering control considering dynamic flow characteristics for wind farm power improvement X. Yu et al. 10.1088/1742-6596/2505/1/012010
- Model predictive control of wakes for wind farm power tracking A. Sterle et al. 10.1088/1742-6596/2767/3/032005
- A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
- Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm J. Liew et al. 10.5194/wes-8-1387-2023
- An integrated deep neural network framework for predicting the wake flow in the wind field S. Sun et al. 10.1016/j.energy.2024.130400
- 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
- Dynamic response of a wind turbine wake subjected to surge and heave step motions under different inflow conditions A. Hubert et al. 10.1088/1742-6596/2767/9/092035
- MPC-Based Fatigue Load Suppression of Waked Wind Farm With 2Dof WT Control Strategy W. Chen et al. 10.1109/TSTE.2024.3407775
- Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
- Study of a dynamic effect-based method for wind farm yaw control optimization L. Li et al. 10.1080/15435075.2023.2297771
- The dynamic coupling between the pulse wake mixing strategy and floating wind turbines D. van den Berg et al. 10.5194/wes-8-849-2023
4 citations as recorded by crossref.
- Development of a dynamic wake model accounting for wake advection delays and mesoscale wind transients B. Foloppe et al. 10.1088/1742-6596/2265/2/022055
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake M. Becker et al. 10.5194/wes-7-2163-2022
- A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
Latest update: 23 Nov 2024
Short summary
In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
In this paper we present a revised dynamic control-oriented wind farm model. The model can...
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