Articles | Volume 5, issue 2
https://doi.org/10.5194/wes-5-647-2020
© Author(s) 2020. 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-5-647-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 May 2020
Research article |
| 27 May 2020
| 27 May 2020
Improving wind farm flow models by learning from operational data
Johannes Schreiber
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
Bastian Salbert
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
Filippo Campagnolo
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
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- The impact of the atmospheric boundary layer on the asymmetric wake profile: A bivariate analysis M. Barasa et al. https://doi.org/10.1016/j.seta.2022.102563
- A physics-based model for wind turbine wake expansion in the atmospheric boundary layer D. Vahidi & F. Porté-Agel https://doi.org/10.1017/jfm.2022.443
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- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
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- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm S. Shid-Moosavi et al. https://doi.org/10.1016/j.renene.2024.122126
- The balance effects of momentum deficit and thrust in cumulative wake models M. Barasa et al. https://doi.org/10.1016/j.energy.2022.123399
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- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
- Analysis of the systematic errors of energy yield assessment in the context of wind farm repowering P. Mazoyer et al. https://doi.org/10.1088/1742-6596/2507/1/012016
- CFD Analysis of the Mechanical Power and the Wake of a Scaled Wind Turbine and Its Experimental Validation Y. Hwang & I. Paek https://doi.org/10.7736/JKSPE.020.113
- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. https://doi.org/10.1063/5.0129481
- Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy B. Doekemeijer et al. https://doi.org/10.5194/wes-6-159-2021
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. https://doi.org/10.5194/wes-7-345-2022
- Adaptive economic wind turbine control A. Anand & C. Bottasso https://doi.org/10.5194/wes-11-1989-2026
- Performance Test of 3D Printed Blades for a Scaled Wind Turbine in a Wind Tunnel D. Kim et al. https://doi.org/10.7736/JKSPE.020.057
- The impact of the atmospheric boundary layer on the asymmetric wake profile: A bivariate analysis M. Barasa et al. https://doi.org/10.1016/j.seta.2022.102563
- A physics-based model for wind turbine wake expansion in the atmospheric boundary layer D. Vahidi & F. Porté-Agel https://doi.org/10.1017/jfm.2022.443
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
- Control-oriented modelling of wind direction variability S. Dallas et al. https://doi.org/10.5194/wes-9-841-2024
- Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. https://doi.org/10.5194/wes-6-737-2021
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. https://doi.org/10.5194/wes-8-691-2023
- Field testing of a local wind inflow estimator and wake detector J. Schreiber et al. https://doi.org/10.5194/wes-5-867-2020
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. https://doi.org/10.5194/wes-5-1273-2020
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. https://doi.org/10.5194/wes-8-747-2023
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland https://doi.org/10.1063/5.0051071
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. https://doi.org/10.1016/j.apenergy.2023.121426
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm S. Shid-Moosavi et al. https://doi.org/10.1016/j.renene.2024.122126
- The balance effects of momentum deficit and thrust in cumulative wake models M. Barasa et al. https://doi.org/10.1016/j.energy.2022.123399
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. https://doi.org/10.5194/wes-5-1315-2020
- Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. https://doi.org/10.1088/1742-6596/2265/2/022019
- FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization M. LoCascio et al. https://doi.org/10.1002/we.2954
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
- Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
- A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers https://doi.org/10.1002/we.2669
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are
learnedfrom operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.
The paper describes a new method that uses standard historical operational data and reconstructs...
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