The wind farm as a sensor: learning and  explaining orographic and plant-induced  flow heterogeneities from operational data

Braunbehrens, Robert; Vad, Andreas; Bottasso, Carlo L.

doi:https://doi.org/10.5194/wes-8-691-2023

Articles | Volume 8, issue 5

https://doi.org/10.5194/wes-8-691-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-8-691-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 8, issue 5

Research article

|

05 May 2023

Research article |

| 05 May 2023

The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data

Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso

Data sets

Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens, A. Vad, and C. L. Bottasso https://doi.org/10.5281/zenodo.7797769

Short summary

The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.