Articles | Volume 2, issue 2
https://doi.org/10.5194/wes-2-615-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/wes-2-615-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Dec 2017
Research article |
| 19 Dec 2017
Wind inflow observation from load harmonics
Marta Bertelè
Wind Energy Institute, Technische Universität München, 85748 Garching
bei München, Germany
Wind Energy Institute, Technische Universität München, 85748 Garching
bei München, Germany
Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano,
20156 Milan, Italy
Stefano Cacciola
Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano,
20156 Milan, Italy
Fabiano Daher Adegas
GE Global Research, 85748 Garching bei München, Germany
Sara Delport
GE Global Research, 85748 Garching bei München, Germany
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Cited
22 citations as recorded by crossref.
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
- Brief communication: Wind inflow observation from load harmonics – wind tunnel validation of the rotationally symmetric formulation M. Bertelè et al. 10.5194/wes-4-89-2019
- Wind inflow observation from load harmonics: initial steps towards a field validation M. Bertelè et al. 10.5194/wes-6-759-2021
- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann 10.5194/wes-4-407-2019
- Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling J. Liew et al. 10.1088/1742-6596/2626/1/012050
- System-level design studies for large rotors D. Zalkind et al. 10.5194/wes-4-595-2019
- Field validation of a yaw misalignment observer for wind farm control M. Bertelè et al. 10.1088/1742-6596/2767/9/092013
- Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering D. Onnen et al. 10.1088/1742-6596/2265/2/022024
- Field testing of a local wind inflow estimator and wake detector J. Schreiber et al. 10.5194/wes-5-867-2020
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. 10.5194/wes-8-1755-2023
- 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
- Non-deterministic wind observation from wind turbine loads M. Bertelè & C. Bottasso 10.1088/1742-6596/1618/6/062022
- Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al. 10.1063/5.0032917
- Wind inflow observation from load harmonics via neural networks: A simulation and field study K. Kim et al. 10.1016/j.renene.2022.12.051
- Wind turbine control: open-source software for control education, standardization and compilation S. Mulders et al. 10.1088/1742-6596/1452/1/012010
- Real‐time rotor effective wind speed estimation based on actuator disc theory: Design and full‐scale experimental validation A. Lio et al. 10.1002/we.2858
- On the robustness of a blade-load-based wind speed estimator to dynamic pitch control strategies M. Coquelet et al. 10.5194/wes-9-1923-2024
- Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling J. Liew et al. 10.1088/1742-6596/2767/9/092039
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Efficient Loads Surrogates for Waked Turbines in an Array K. Shaler et al. 10.1088/1742-6596/2265/3/032095
- 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
- Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections S. Perez-Becker et al. 10.5194/wes-6-791-2021
22 citations as recorded by crossref.
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
- Brief communication: Wind inflow observation from load harmonics – wind tunnel validation of the rotationally symmetric formulation M. Bertelè et al. 10.5194/wes-4-89-2019
- Wind inflow observation from load harmonics: initial steps towards a field validation M. Bertelè et al. 10.5194/wes-6-759-2021
- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann 10.5194/wes-4-407-2019
- Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling J. Liew et al. 10.1088/1742-6596/2626/1/012050
- System-level design studies for large rotors D. Zalkind et al. 10.5194/wes-4-595-2019
- Field validation of a yaw misalignment observer for wind farm control M. Bertelè et al. 10.1088/1742-6596/2767/9/092013
- Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering D. Onnen et al. 10.1088/1742-6596/2265/2/022024
- Field testing of a local wind inflow estimator and wake detector J. Schreiber et al. 10.5194/wes-5-867-2020
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. 10.5194/wes-8-1755-2023
- 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
- Non-deterministic wind observation from wind turbine loads M. Bertelè & C. Bottasso 10.1088/1742-6596/1618/6/062022
- Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al. 10.1063/5.0032917
- Wind inflow observation from load harmonics via neural networks: A simulation and field study K. Kim et al. 10.1016/j.renene.2022.12.051
- Wind turbine control: open-source software for control education, standardization and compilation S. Mulders et al. 10.1088/1742-6596/1452/1/012010
- Real‐time rotor effective wind speed estimation based on actuator disc theory: Design and full‐scale experimental validation A. Lio et al. 10.1002/we.2858
- On the robustness of a blade-load-based wind speed estimator to dynamic pitch control strategies M. Coquelet et al. 10.5194/wes-9-1923-2024
- Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling J. Liew et al. 10.1088/1742-6596/2767/9/092039
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Efficient Loads Surrogates for Waked Turbines in an Array K. Shaler et al. 10.1088/1742-6596/2265/3/032095
- 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
- Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections S. Perez-Becker et al. 10.5194/wes-6-791-2021
Latest update: 22 Nov 2024
Short summary
The rotor of a wind turbine is used to determine some important parameters of the wind, including the direction of the wind vector relative to the rotor disk and horizontal and vertical shears. The method works by using measurements provided by existing onboard load sensors. The observed wind characteristics can be used to implement advanced features in smart wind turbine and wind farm controllers.
The rotor of a wind turbine is used to determine some important parameters of the wind,...
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