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
| 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
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Cited
61 citations as recorded by crossref.
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- The restricted nonlinear large eddy simulation approach to reduced-order wind farm modeling J. Bretheim et al. 10.1063/1.5026325
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- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT) V. Fazlollahi et al. 10.1177/0309524X231199432
- Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur 10.3390/app12084013
- Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
- Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning H. Dong & X. Zhao 10.1109/TII.2023.3252540
- LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan 10.3390/su11246939
- 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
- The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
- 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
- Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms* K. Hoang et al. 10.1016/j.ifacol.2023.10.1164
- Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao 10.1016/j.enconman.2023.117507
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
- 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
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
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- 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
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- Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
- 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
- Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning H. Dong & X. Zhao 10.1109/TCST.2022.3223185
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- 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
- 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
- Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data R. Li et al. 10.1016/j.energy.2022.124845
- Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
- 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
- Progress on Offshore Wind Farm Dynamic Wake Management for Energy L. Zhao et al. 10.3390/jmse10101395
- Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
- Wind farm control technologies: from classical control to reinforcement learning H. Dong et al. 10.1088/2516-1083/ac6cc1
- Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al. 10.1016/j.enconman.2023.117031
- Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. 10.3390/en16093932
- pymodconn: A python package for developing modular sequence-to-sequence control-oriented deep neural networks G. Chaudhary et al. 10.1016/j.softx.2023.101599
- A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
- Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test X. Gao et al. 10.1016/j.energy.2022.124277
- 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
- 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
- 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
- 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
- Towards real-time optimal control of wind farms using large-eddy simulations N. Janssens & J. Meyers 10.5194/wes-9-65-2024
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300
61 citations as recorded by crossref.
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
- 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
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control H. Dong & X. Zhao 10.1109/TCST.2021.3102476
- The restricted nonlinear large eddy simulation approach to reduced-order wind farm modeling J. Bretheim et al. 10.1063/1.5026325
- Optimal combined wake and active power control of large‐scale wind farm considering available power W. Chen et al. 10.1049/rpg2.12883
- Improving wind farm flow models by learning from operational data J. Schreiber et al. 10.5194/wes-5-647-2020
- Model predictive control strategy in waked wind farms for optimal fatigue loads C. Zhong et al. 10.1016/j.epsr.2023.109793
- 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 review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT) V. Fazlollahi et al. 10.1177/0309524X231199432
- Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur 10.3390/app12084013
- Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
- Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning H. Dong & X. Zhao 10.1109/TII.2023.3252540
- LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan 10.3390/su11246939
- 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
- The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
- 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
- Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms* K. Hoang et al. 10.1016/j.ifacol.2023.10.1164
- Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao 10.1016/j.enconman.2023.117507
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
- 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
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
- 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
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
- 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
- Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning H. Dong & X. Zhao 10.1109/TCST.2022.3223185
- 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
- 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
- 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
- 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
- Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data R. Li et al. 10.1016/j.energy.2022.124845
- Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
- 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
- Progress on Offshore Wind Farm Dynamic Wake Management for Energy L. Zhao et al. 10.3390/jmse10101395
- Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
- Wind farm control technologies: from classical control to reinforcement learning H. Dong et al. 10.1088/2516-1083/ac6cc1
- Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al. 10.1016/j.enconman.2023.117031
- Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. 10.3390/en16093932
- pymodconn: A python package for developing modular sequence-to-sequence control-oriented deep neural networks G. Chaudhary et al. 10.1016/j.softx.2023.101599
- A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
- Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test X. Gao et al. 10.1016/j.energy.2022.124277
- 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
- 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
- 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
- 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
- Towards real-time optimal control of wind farms using large-eddy simulations N. Janssens & J. Meyers 10.5194/wes-9-65-2024
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300
Latest update: 23 Apr 2024
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...
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