Articles | Volume 2, issue 1
https://doi.org/10.5194/wes-2-257-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/wes-2-257-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 May 2017
Research article |
| 23 May 2017
Lidar-based wake tracking for closed-loop wind farm control
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
Po Wen Cheng
Stuttgart Wind Energy (SWE), University of Stuttgart,
Allmandring 5B, 70569 Stuttgart, Germany
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Cited
20 citations as recorded by crossref.
- Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT) V. Fazlollahi et al. https://doi.org/10.1177/0309524X231199432
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. https://doi.org/10.1016/j.conengprac.2018.11.005
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. https://doi.org/10.1002/we.2852
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. https://doi.org/10.3390/en16196908
- IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar A. Clifton et al. https://doi.org/10.3390/rs10030406
- A control-oriented dynamic wind farm model: WFSim S. Boersma et al. https://doi.org/10.5194/wes-3-75-2018
- Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals D. Conti et al. https://doi.org/10.5194/wes-6-841-2021
- Lidar-enhanced closed-loop active helix approach Z. Chen et al. https://doi.org/10.5194/wes-11-1871-2026
- Wake detection in the turbine inflow using nacelle lidars D. Held et al. https://doi.org/10.1088/1742-6596/1102/1/012005
- Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
- Spatially distributed LPV control framework over arbitrary localized areas for collaborative load reduction in wind farms V. Fazlollahi et al. https://doi.org/10.1016/j.conengprac.2026.107149
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- SuperOB: Super-resolution flow reconstruction from sparse measurements via re-Orthogonalization of a global Basis S. Andersen & J. Leon https://doi.org/10.1088/1742-6596/3224/3/032140
- Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. https://doi.org/10.1088/1742-6596/1452/1/012012
- 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
- Robust wake steering control design in a wind farm for power optimisation using adaptive learning game theory (ALGT) method V. Fazlollahi et al. https://doi.org/10.1080/00207179.2021.2009558
- 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
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. https://doi.org/10.3389/fmech.2023.1261017
- Modern Strategies for Controlling Wind Power Plants: Technologies, Challenges and Prospects N. Kurylko & R. Fedoryshyn https://doi.org/10.23939/jeecs2024.01.056
- 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
20 citations as recorded by crossref.
- Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT) V. Fazlollahi et al. https://doi.org/10.1177/0309524X231199432
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. https://doi.org/10.1016/j.conengprac.2018.11.005
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. https://doi.org/10.1002/we.2852
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. https://doi.org/10.3390/en16196908
- IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar A. Clifton et al. https://doi.org/10.3390/rs10030406
- A control-oriented dynamic wind farm model: WFSim S. Boersma et al. https://doi.org/10.5194/wes-3-75-2018
- Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals D. Conti et al. https://doi.org/10.5194/wes-6-841-2021
- Lidar-enhanced closed-loop active helix approach Z. Chen et al. https://doi.org/10.5194/wes-11-1871-2026
- Wake detection in the turbine inflow using nacelle lidars D. Held et al. https://doi.org/10.1088/1742-6596/1102/1/012005
- Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
- Spatially distributed LPV control framework over arbitrary localized areas for collaborative load reduction in wind farms V. Fazlollahi et al. https://doi.org/10.1016/j.conengprac.2026.107149
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
- SuperOB: Super-resolution flow reconstruction from sparse measurements via re-Orthogonalization of a global Basis S. Andersen & J. Leon https://doi.org/10.1088/1742-6596/3224/3/032140
- Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. https://doi.org/10.1088/1742-6596/1452/1/012012
- 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
- Robust wake steering control design in a wind farm for power optimisation using adaptive learning game theory (ALGT) method V. Fazlollahi et al. https://doi.org/10.1080/00207179.2021.2009558
- 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
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. https://doi.org/10.3389/fmech.2023.1261017
- Modern Strategies for Controlling Wind Power Plants: Technologies, Challenges and Prospects N. Kurylko & R. Fedoryshyn https://doi.org/10.23939/jeecs2024.01.056
- 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
Saved (final revised paper)
Latest update: 26 Aug 2026
Short summary
This work provides a possible solution to closed-loop flow control in a wind farm.
The remote sensing technology, lidar, which is a laser-based measurement system, is used to obtain wind speed information behind a wind turbine. The measurements are processed using a model-based approach to estimate position information of the wake. The information is then used in a controller to redirect the wake to the desired position. Altogether, the concept aims to increase the power output of a wind farm.
This work provides a possible solution to closed-loop flow control in a wind farm.
The remote...
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