Lidar-based wake tracking for closed-loop wind farm control

Raach, Steffen; Schlipf, David; Cheng, Po Wen

doi:10.5194/wes-2-257-2017

Cited by 20 publications

(27 citation statements)

References 12 publications

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“…This model can be used in conjunction with the field data to perform closed-loop wind farm controls, as performed in Raach et al (2016). In addition, future work could use the simulated v LOS , which is computed using this lidar model to fill in gaps that are inevitably present in real-time lidar data.…”

Section: Turbine Modelmentioning

confidence: 99%

“…Additional details are provided in Raach et al (2016). This model can be used in conjunction with the field data to perform closed-loop wind farm controls, as performed in Raach et al (2016).…”

Section: Turbine Modelmentioning

confidence: 99%

“…The scan rate is dependent on the number of pulses used for each 10 measurement position. The lidar system has been used for lidar-assisted control by applying inflow and wake measurements (see Raach et al (2016)). …”

Section: Lidar Specificationsmentioning

confidence: 99%

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Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

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Abstract.Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream 5 turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. 10This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.

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Section: Turbine Modelmentioning

confidence: 99%

Section: Turbine Modelmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the future, when using lidar measurement data to estimate the wake position, more advanced methods like a model-based wake tracking approach is needed (see e.g. Raach et al (2016b)). …”

Section: Wake Tracking In Wfsimmentioning

confidence: 99%

“…1. The concept can be divided in an estimation task and a control task, see Raach et al (2016b). The estimation task deals with processing lidar measurement data to useful information for the controller.…”

Section: Introductionmentioning

confidence: 99%

Robust lidar-based closed-loop wake redirection for wind farm control

et al. 2017

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Wind turbine wake redirection is a promising concept for wind farm control to increase the total power of a wind farm. Further, the concept aims to avoid partial wake overlap on a downwind wind turbine and hence aims to decrease structural loads. Controller for wake redirection need to account for model uncertainties due to the complexity of wake dynamics. Therefore, this work focuses first on modeling a wind farm using an uncertain plant description and second on the design of a robust H ∞ controller for closed-loop wake redirection by applying standard robust modeling and control techniques on a wind farm. The wake center position is estimated and fed back to a controller which uses the yaw actuator to redirect the wake. For several inflow conditions, step simulations are conducted and system identifications are performed to obtain multiple plant models. This set of models is used to derive a nominal plant and an uncertainty set. Both the nominal model and the uncertainty set define the uncertain plant model. The robust controller is then designed showing promising results in a medium-fidelity CFD simulation model with time-varying inflow conditions.

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Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares

Liew,

Göçmen,

Lio

et al. 2023

Wind Energy

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Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of and (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems.

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Lidar-based wake tracking for closed-loop wind farm control

Cited by 20 publications

References 12 publications

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Robust lidar-based closed-loop wake redirection for wind farm control

Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares

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