Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-867-2020
© 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-5-867-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jul 2020
Research article |
| 07 Jul 2020
| 07 Jul 2020
Field testing of a local wind inflow estimator and wake detector
Johannes Schreiber
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
Marta Bertelè
Wind Energy Institute, Technische Universität München, 85748
Garching bei München, Germany
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Cited
23 citations as recorded by crossref.
- Digital shadows with optimized bias correction for accurate wind turbine fatigue load estimation H. Hoghooghi & C. Bottasso https://doi.org/10.1088/1742-6596/3224/6/062003
- A wind turbine digital shadow for complex inflow conditions H. Hoghooghi & C. Bottasso https://doi.org/10.5194/wes-11-373-2026
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Closed-loop coupling of a dynamic wake model with a wind inflow estimator J. Di Cave et al. https://doi.org/10.1088/1742-6596/2767/3/032034
- Wind turbine inflow estimation via nested, self-calibrating EKF: Design and field test D. Onnen et al. https://doi.org/10.1016/j.conengprac.2026.106898
- A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines A. Guilloré et al. https://doi.org/10.1088/1742-6596/2767/3/032019
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. https://doi.org/10.1088/1742-6596/2767/3/032026
- LQ Optimal Control for Power Tracking Operation of Wind Turbines A. Grapentin et al. https://doi.org/10.1016/j.ifacol.2023.10.1374
- Field comparison of load-based wind turbine wake tracking with a scanning lidar reference D. Onnen et al. https://doi.org/10.5194/wes-11-175-2026
- 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
- Wind turbine wake detection and characterisation utilising blade loads and SCADA data: a generalised approach P. Fojcik et al. https://doi.org/10.5194/wes-10-1943-2025
- Wind inflow observation from load harmonics via neural networks: A simulation and field study K. Kim et al. https://doi.org/10.1016/j.renene.2022.12.051
- Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation P. Meyer & J. Gottschall https://doi.org/10.1088/1742-6596/2265/2/022107
- Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering D. Onnen et al. https://doi.org/10.1088/1742-6596/2265/2/022024
- Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and yaw actuation E. Nanos et al. https://doi.org/10.5194/wes-7-1263-2022
- Towards the multi-scale Kalman filtering of dynamic wake models: observing turbulent fluctuations and wake meandering R. Braunbehrens et al. https://doi.org/10.1088/1742-6596/2505/1/012044
- The rotor as a sensor – observing shear and veer from the operational data of a large wind turbine M. Bertelè et al. https://doi.org/10.5194/wes-9-1419-2024
- Wind inflow observation from load harmonics: initial steps towards a field validation M. Bertelè et al. https://doi.org/10.5194/wes-6-759-2021
- First experimental results on lifetime-aware wind farm control R. Braunbehrens et al. https://doi.org/10.1088/1742-6596/2767/3/032042
- Virtual sensors for wind turbines with machine learning‐based time series models N. Dimitrov & T. Göçmen https://doi.org/10.1002/we.2762
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. https://doi.org/10.5194/wes-8-1755-2023
- Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. https://doi.org/10.3389/fenrg.2022.883889
- Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I. Eguinoa et al. https://doi.org/10.1002/adc2.80
23 citations as recorded by crossref.
- Digital shadows with optimized bias correction for accurate wind turbine fatigue load estimation H. Hoghooghi & C. Bottasso https://doi.org/10.1088/1742-6596/3224/6/062003
- A wind turbine digital shadow for complex inflow conditions H. Hoghooghi & C. Bottasso https://doi.org/10.5194/wes-11-373-2026
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- Closed-loop coupling of a dynamic wake model with a wind inflow estimator J. Di Cave et al. https://doi.org/10.1088/1742-6596/2767/3/032034
- Wind turbine inflow estimation via nested, self-calibrating EKF: Design and field test D. Onnen et al. https://doi.org/10.1016/j.conengprac.2026.106898
- A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines A. Guilloré et al. https://doi.org/10.1088/1742-6596/2767/3/032019
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. https://doi.org/10.1088/1742-6596/2767/3/032026
- LQ Optimal Control for Power Tracking Operation of Wind Turbines A. Grapentin et al. https://doi.org/10.1016/j.ifacol.2023.10.1374
- Field comparison of load-based wind turbine wake tracking with a scanning lidar reference D. Onnen et al. https://doi.org/10.5194/wes-11-175-2026
- 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
- Wind turbine wake detection and characterisation utilising blade loads and SCADA data: a generalised approach P. Fojcik et al. https://doi.org/10.5194/wes-10-1943-2025
- Wind inflow observation from load harmonics via neural networks: A simulation and field study K. Kim et al. https://doi.org/10.1016/j.renene.2022.12.051
- Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation P. Meyer & J. Gottschall https://doi.org/10.1088/1742-6596/2265/2/022107
- Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering D. Onnen et al. https://doi.org/10.1088/1742-6596/2265/2/022024
- Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and yaw actuation E. Nanos et al. https://doi.org/10.5194/wes-7-1263-2022
- Towards the multi-scale Kalman filtering of dynamic wake models: observing turbulent fluctuations and wake meandering R. Braunbehrens et al. https://doi.org/10.1088/1742-6596/2505/1/012044
- The rotor as a sensor – observing shear and veer from the operational data of a large wind turbine M. Bertelè et al. https://doi.org/10.5194/wes-9-1419-2024
- Wind inflow observation from load harmonics: initial steps towards a field validation M. Bertelè et al. https://doi.org/10.5194/wes-6-759-2021
- First experimental results on lifetime-aware wind farm control R. Braunbehrens et al. https://doi.org/10.1088/1742-6596/2767/3/032042
- Virtual sensors for wind turbines with machine learning‐based time series models N. Dimitrov & T. Göçmen https://doi.org/10.1002/we.2762
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. https://doi.org/10.5194/wes-8-1755-2023
- Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. https://doi.org/10.3389/fenrg.2022.883889
- Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I. Eguinoa et al. https://doi.org/10.1002/adc2.80
Saved (final revised paper)
Latest update: 14 Aug 2026
Short summary
This paper validates a method to estimate the vertical wind shear and detect the presence and location of an impinging wake with field data. Shear and wake awareness have multiple uses, from turbine and farm control to monitoring and forecasting.
Results indicate a very good correlation between the estimated vertical shear and the one measured by a met mast and a remarkable ability to locate and track the motion of an impinging wake on an affected rotor.
This paper validates a method to estimate the vertical wind shear and detect the presence and...
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