Articles | Volume 11, issue 5
https://doi.org/10.5194/wes-11-1771-2026
© Author(s) 2026. 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-11-1771-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 May 2026
Research article |
| 19 May 2026
| 19 May 2026
A two-stage framework for identifying and characterizing wind turbine noise data and its validation by listening tests
Susanne Könecke
CORRESPONDING AUTHOR
Leibniz University Hannover, Institute of Structural Analysis/ForWind, Appelstraße 9A, 30167 Hannover, Germany
Clemens Jonscher
Leibniz University Hannover, Institute of Structural Analysis/ForWind, Appelstraße 9A, 30167 Hannover, Germany
Tobias Bohne
Leibniz University Hannover, Institute of Structural Analysis/ForWind, Appelstraße 9A, 30167 Hannover, Germany
Raimund Rolfes
Leibniz University Hannover, Institute of Structural Analysis/ForWind, Appelstraße 9A, 30167 Hannover, Germany
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Short summary
This paper presents a framework to identify wind turbine noise in long-term field measurements. By combining statistical criteria, turbine operating data, and physics-based signal analysis, periods dominated by wind turbine noise and its key components are detected. The framework is validated using a structured listening test and applied to a 1-month dataset. The framework, listening-test platform, and anonymized audio data are publicly available to support further research.
This paper presents a framework to identify wind turbine noise in long-term field measurements....
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