Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-819-2018
© 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-3-819-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2018
Research article |
| 01 Nov 2018
| 01 Nov 2018
Analysis of control-oriented wake modeling tools using lidar field results
Jennifer Annoni
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy
Laboratory, Golden, CO, 80401, USA
Paul Fleming
National Wind Technology Center, National Renewable Energy
Laboratory, Golden, CO, 80401, USA
Andrew Scholbrock
National Wind Technology Center, National Renewable Energy
Laboratory, Golden, CO, 80401, USA
Jason Roadman
National Wind Technology Center, National Renewable Energy
Laboratory, Golden, CO, 80401, USA
Scott Dana
National Wind Technology Center, National Renewable Energy
Laboratory, Golden, CO, 80401, USA
Christiane Adcock
National Wind Technology Center, National Renewable Energy
Laboratory, Golden, CO, 80401, USA
Fernando Porte-Agel
Ecole Polytechnique Federale de
Lausanne (EPFL), Wind Engineering and Renewable Energy Laboratory (WIRE),
EPFL-ENAC-IIE-WIRE, 1015 Lausanne, Switzerland
Steffen Raach
Stuttgart Wind
Energy (SWE), University of Stuttgart, Allmandring 5B, 70569 Stuttgart,
Germany
Florian Haizmann
Stuttgart Wind
Energy (SWE), University of Stuttgart, Allmandring 5B, 70569 Stuttgart,
Germany
David Schlipf
Stuttgart Wind
Energy (SWE), University of Stuttgart, Allmandring 5B, 70569 Stuttgart,
Germany
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- Wake impact of constructing a new offshore wind farm zone on an existing downwind cluster: a case study of the Belgian Princess Elisabeth zone using FLORIS W. Munters et al. 10.1088/1742-6596/2265/2/022049
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
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- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
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- Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments J. Frederik et al. 10.5194/wes-5-245-2020
- Analytical three‐dimensional wind flow model for real‐time wind farm simulation V. Kipke et al. 10.1049/iet-rpg.2020.0106
- The aerodynamics of the curled wake: a simplified model in view of flow control L. Martínez-Tossas et al. 10.5194/wes-4-127-2019
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Numerical Validation of Wind Plant Control Strategies S. Gomez-Iradi et al. 10.1088/1742-6596/1618/2/022010
- Cluster-based probabilistic structure dynamical model of wind turbine wake N. Ali et al. 10.1080/14685248.2021.1925125
- An algorithm for measuring wind speed based on sampling aerosol inhomogeneities P. Filimonov et al. 10.18287/2412-6179-CO-708
- Fast Processing Intelligent Wind Farm Controller for Production Maximisation T. Ahmad et al. 10.3390/en12030544
- A simulation study demonstrating the importance of large-scale trailing vortices in wake steering P. Fleming et al. 10.5194/wes-3-243-2018
- Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri 10.3390/en12142716
68 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
- Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland 10.5194/wes-5-885-2020
- Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
- Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
- Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes N. Ali et al. 10.1063/5.0036281
- Joint optimization of wind farm layout considering optimal control K. Chen et al. 10.1016/j.renene.2021.10.032
- Operation assessment of analytical wind turbine wake models A. Lopes et al. 10.1016/j.jweia.2021.104840
- Review of atmospheric stability estimations for wind power applications C. Pérez Albornoz et al. 10.1016/j.rser.2022.112505
- Aerodynamic characterization of two tandem wind turbines under yaw misalignment control using actuator line model Y. Tu et al. 10.1016/j.oceaneng.2023.114992
- Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al. 10.31875/2409-9694.2023.10.02
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Opportunities for green hydrogen production with land-based wind in the United States C. Clark et al. 10.1016/j.enconman.2023.117595
- Wind farm active wake control via concurrent yaw and tip-speed ratio optimization A. Hosseini et al. 10.1016/j.apenergy.2024.124625
- Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. 10.5194/wes-5-309-2020
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
- A data-driven analytical model for wind turbine wakes using machine learning method G. Nai-Zhi et al. 10.1016/j.enconman.2021.115130
- Assessing the blockage effect of wind turbines and wind farms using an analytical vortex model E. Branlard & A. Meyer Forsting 10.1002/we.2546
- Wind farm blockage effects: comparison of different engineering models E. Branlard et al. 10.1088/1742-6596/1618/6/062036
- The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms J. Frederik et al. 10.1002/we.2513
- Investigating Suppression of Cloud Return with a Novel Optical Configuration of a Doppler Lidar L. Jin et al. 10.3390/rs14153576
- 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
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows L. Martínez-Tossas et al. 10.5194/wes-6-555-2021
- Wind Farm Loads under Wake Redirection Control S. Kanev et al. 10.3390/en13164088
- Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
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- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. 10.5194/wes-7-1941-2022
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- The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
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- Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production M. Ichenial et al. 10.1177/0309524X19880929
- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
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- Coordinated wind power plant and battery control for active power services M. Sinner et al. 10.1063/5.0156464
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- A physics-inspired neural network model for short-term wind power prediction considering wake effects N. Guo et al. 10.1016/j.energy.2022.125208
- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
- A multi-fidelity framework for power prediction of wind farm under yaw misalignment Y. Tu et al. 10.1016/j.apenergy.2024.124600
- A Comparative Study of Multi-Stage Approaches for Wind Farm Layout Optimization P. Yang & H. Najafi 10.1115/1.4053869
- Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models P. Hulsman et al. 10.1088/1742-6596/1618/2/022056
- Wake impact of constructing a new offshore wind farm zone on an existing downwind cluster: a case study of the Belgian Princess Elisabeth zone using FLORIS W. Munters et al. 10.1088/1742-6596/2265/2/022049
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization X. Yin et al. 10.1016/j.renene.2020.05.015
- End-to-end wind turbine wake modelling with deep graph representation learning S. Li et al. 10.1016/j.apenergy.2023.120928
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. 10.3390/app12157392
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
- Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1 P. Fleming et al. 10.5194/wes-4-273-2019
- Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments J. Frederik et al. 10.5194/wes-5-245-2020
- Analytical three‐dimensional wind flow model for real‐time wind farm simulation V. Kipke et al. 10.1049/iet-rpg.2020.0106
- The aerodynamics of the curled wake: a simplified model in view of flow control L. Martínez-Tossas et al. 10.5194/wes-4-127-2019
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
6 citations as recorded by crossref.
- Numerical Validation of Wind Plant Control Strategies S. Gomez-Iradi et al. 10.1088/1742-6596/1618/2/022010
- Cluster-based probabilistic structure dynamical model of wind turbine wake N. Ali et al. 10.1080/14685248.2021.1925125
- An algorithm for measuring wind speed based on sampling aerosol inhomogeneities P. Filimonov et al. 10.18287/2412-6179-CO-708
- Fast Processing Intelligent Wind Farm Controller for Production Maximisation T. Ahmad et al. 10.3390/en12030544
- A simulation study demonstrating the importance of large-scale trailing vortices in wake steering P. Fleming et al. 10.5194/wes-3-243-2018
- Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri 10.3390/en12142716
Latest update: 20 Nov 2024
Short summary
This paper addresses the modeling aspect of wind farm control. To implement successful wind farm controls, a suitable model has to be used that captures the relevant physics. This paper addresses three different wake models that can be used for controls and compares these models with lidar field data from a utility-scale turbine.
This paper addresses the modeling aspect of wind farm control. To implement successful wind farm...
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