Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-749-2018
© 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-3-749-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Oct 2018
Research article |
| 24 Oct 2018
| 24 Oct 2018
Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control
Bart M. Doekemeijer
CORRESPONDING AUTHOR
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Sjoerd Boersma
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Lucy Y. Pao
Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Torben Knudsen
Department of Electronic Systems, Aalborg University, Aalborg, Denmark
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
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Cited
24 citations as recorded by crossref.
- Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. 10.1063/5.0039899
- Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al. 10.1109/TCST.2021.3053776
- Data assimilation for online model calibration in discrete event simulation X. Hu & M. Yan 10.1177/00375497231221578
- 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
- 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
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- 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
- Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models B. Doekemeijer & J. van Wingerden 10.1002/we.2495
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- Progress on Offshore Wind Farm Dynamic Wake Management for Energy L. Zhao et al. 10.3390/jmse10101395
- Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control A. Dittmer et al. 10.1016/j.ifacol.2023.10.1037
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control H. Dong & X. Zhao 10.1109/TCST.2021.3102476
- Model-Guided Learning for Wind Farm Power Optimization Z. Xu et al. 10.1109/TCST.2023.3315547
- Three-dimensional stochastic dynamical modeling for wind farm flow estimation M. Lingad et al. 10.1088/1742-6596/2767/5/052065
- Short-term wind forecasting via surface pressure measurements: Stochastic modeling and sensor placement S. Abootorabi et al. 10.1063/5.0216465
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. 10.1016/j.rser.2025.115605
- Utilizing WFSim to Investigate the Impact of Optimal Wind Farm Layout and Inter-Field Wake on Average Power G. Li et al. 10.3390/jmse12081353
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
- A constrained model predictive wind farm controller providing active power control: an LES study S. Boersma et al. 10.1088/1742-6596/1037/3/032023
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
21 citations as recorded by crossref.
- Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. 10.1063/5.0039899
- Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al. 10.1109/TCST.2021.3053776
- Data assimilation for online model calibration in discrete event simulation X. Hu & M. Yan 10.1177/00375497231221578
- 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
- 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
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- 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
- Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models B. Doekemeijer & J. van Wingerden 10.1002/we.2495
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- Progress on Offshore Wind Farm Dynamic Wake Management for Energy L. Zhao et al. 10.3390/jmse10101395
- Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control A. Dittmer et al. 10.1016/j.ifacol.2023.10.1037
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control H. Dong & X. Zhao 10.1109/TCST.2021.3102476
- Model-Guided Learning for Wind Farm Power Optimization Z. Xu et al. 10.1109/TCST.2023.3315547
- Three-dimensional stochastic dynamical modeling for wind farm flow estimation M. Lingad et al. 10.1088/1742-6596/2767/5/052065
- Short-term wind forecasting via surface pressure measurements: Stochastic modeling and sensor placement S. Abootorabi et al. 10.1063/5.0216465
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. 10.1016/j.rser.2025.115605
- Utilizing WFSim to Investigate the Impact of Optimal Wind Farm Layout and Inter-Field Wake on Average Power G. Li et al. 10.3390/jmse12081353
3 citations as recorded by crossref.
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
- A constrained model predictive wind farm controller providing active power control: an LES study S. Boersma et al. 10.1088/1742-6596/1037/3/032023
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
Latest update: 03 Jul 2025
Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
Most wind farm control algorithms in the literature rely on a simplified mathematical model that...
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