Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-75-2018
© Author(s) 2018. 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-3-75-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Mar 2018
Research article |
| 06 Mar 2018
A control-oriented dynamic wind farm model: WFSim
Sjoerd Boersma
CORRESPONDING AUTHOR
Delft University of Technology, Delft Center for Systems and Control, Mekelweg 2, 2628 CC, Delft, the Netherlands
Bart Doekemeijer
Delft University of Technology, Delft Center for Systems and Control, Mekelweg 2, 2628 CC, Delft, the Netherlands
Mehdi Vali
Wind Energy System Research Group, ForWind, Küpkersweg 70, 26129 Oldenburg, Germany
Johan Meyers
KU Leuven, Department of Mechanical Engineering, Celestijnenlaan 300A, B3001 Leuven, Belgium
Jan-Willem van Wingerden
Delft University of Technology, Delft Center for Systems and Control, Mekelweg 2, 2628 CC, Delft, the Netherlands
Viewed
Total article views: 5,753 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Oct 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,646 | 1,981 | 126 | 5,753 | 122 | 95 |
- HTML: 3,646
- PDF: 1,981
- XML: 126
- Total: 5,753
- BibTeX: 122
- EndNote: 95
Total article views: 4,309 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Mar 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,638 | 1,554 | 117 | 4,309 | 111 | 86 |
- HTML: 2,638
- PDF: 1,554
- XML: 117
- Total: 4,309
- BibTeX: 111
- EndNote: 86
Total article views: 1,444 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Oct 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,008 | 427 | 9 | 1,444 | 11 | 9 |
- HTML: 1,008
- PDF: 427
- XML: 9
- Total: 1,444
- BibTeX: 11
- EndNote: 9
Viewed (geographical distribution)
Total article views: 5,753 (including HTML, PDF, and XML)
Thereof 4,746 with geography defined
and 1,007 with unknown origin.
Total article views: 4,309 (including HTML, PDF, and XML)
Thereof 3,377 with geography defined
and 932 with unknown origin.
Total article views: 1,444 (including HTML, PDF, and XML)
Thereof 1,369 with geography defined
and 75 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
31 citations as recorded by crossref.
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- The restricted nonlinear large eddy simulation approach to reduced-order wind farm modeling J. Bretheim et al. 10.1063/1.5026325
- Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
- Improving wind farm flow models by learning from operational data J. Schreiber et al. 10.5194/wes-5-647-2020
- A multi-objective predictive control strategy for enhancing primary frequency support with wind farms S. Siniscalchi-Minna et al. 10.1088/1742-6596/1037/3/032034
- Model-Optimized Dispatch for Closed-Loop Power Control of Waked Wind Farms J. Kazda & N. Cutululis 10.1109/TCST.2019.2923779
- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
- A novel dynamic wind farm wake model based on deep learning J. Zhang & X. Zhao 10.1016/j.apenergy.2020.115552
- Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation J. Kazda & N. Cutululis 10.3390/en11123346
- Large-eddy simulation study of wind farm active power control with a coordinated load distribution M. Vali et al. 10.1088/1742-6596/1037/3/032018
- LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan 10.3390/su11246939
- Deep Neural Learning Based Distributed Predictive Control for Offshore Wind Farm Using High-Fidelity LES Data X. Yin & X. Zhao 10.1109/TIE.2020.2979560
- A constrained wind farm controller providing secondary frequency regulation: An LES study S. Boersma et al. 10.1016/j.renene.2018.11.031
- Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
- An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer M. Vali et al. 10.5194/wes-4-139-2019
- Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
- The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
- A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. 10.3390/en12152956
- Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. 10.1016/j.apenergy.2021.116928
- Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. 10.1016/j.apenergy.2020.116149
- Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al. 10.1109/TCST.2021.3053776
- A Simulation Model for Providing Analysis of Wind Farms Frequency and Voltage Regulation Services in an Electrical Power System H. Bialas et al. 10.3390/en14082250
- Study on equivalent fatigue damage of two in-a-line wind turbines under yaw-based optimum control H. Meng et al. 10.1080/15435075.2021.2023887
- Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
- A low-fidelity dynamic wind farm model for simulating time-varying wind conditions and floating platform motion A. Kheirabadi & R. Nagamune 10.1016/j.oceaneng.2021.109313
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
- Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300
- Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al. 10.1088/1742-6596/1037/3/032013
- Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control B. Doekemeijer et al. 10.5194/wes-3-749-2018
- Deep learning-aided model predictive control of wind farms for AGC considering the dynamic wake effect K. Chen et al. 10.1016/j.conengprac.2021.104925
31 citations as recorded by crossref.
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- The restricted nonlinear large eddy simulation approach to reduced-order wind farm modeling J. Bretheim et al. 10.1063/1.5026325
- Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
- Improving wind farm flow models by learning from operational data J. Schreiber et al. 10.5194/wes-5-647-2020
- A multi-objective predictive control strategy for enhancing primary frequency support with wind farms S. Siniscalchi-Minna et al. 10.1088/1742-6596/1037/3/032034
- Model-Optimized Dispatch for Closed-Loop Power Control of Waked Wind Farms J. Kazda & N. Cutululis 10.1109/TCST.2019.2923779
- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
- A novel dynamic wind farm wake model based on deep learning J. Zhang & X. Zhao 10.1016/j.apenergy.2020.115552
- Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation J. Kazda & N. Cutululis 10.3390/en11123346
- Large-eddy simulation study of wind farm active power control with a coordinated load distribution M. Vali et al. 10.1088/1742-6596/1037/3/032018
- LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan 10.3390/su11246939
- Deep Neural Learning Based Distributed Predictive Control for Offshore Wind Farm Using High-Fidelity LES Data X. Yin & X. Zhao 10.1109/TIE.2020.2979560
- A constrained wind farm controller providing secondary frequency regulation: An LES study S. Boersma et al. 10.1016/j.renene.2018.11.031
- Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
- An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer M. Vali et al. 10.5194/wes-4-139-2019
- Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
- The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
- A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. 10.3390/en12152956
- Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. 10.1016/j.apenergy.2021.116928
- Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. 10.1016/j.apenergy.2020.116149
- Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al. 10.1109/TCST.2021.3053776
- A Simulation Model for Providing Analysis of Wind Farms Frequency and Voltage Regulation Services in an Electrical Power System H. Bialas et al. 10.3390/en14082250
- Study on equivalent fatigue damage of two in-a-line wind turbines under yaw-based optimum control H. Meng et al. 10.1080/15435075.2021.2023887
- Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
- A low-fidelity dynamic wind farm model for simulating time-varying wind conditions and floating platform motion A. Kheirabadi & R. Nagamune 10.1016/j.oceaneng.2021.109313
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
- Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300
- Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al. 10.1088/1742-6596/1037/3/032013
- Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control B. Doekemeijer et al. 10.5194/wes-3-749-2018
- Deep learning-aided model predictive control of wind farms for AGC considering the dynamic wake effect K. Chen et al. 10.1016/j.conengprac.2021.104925
Latest update: 10 Jun 2023
Short summary
Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities...
