Psychoacoustic model for predicting wind turbine noise annoyance

Psychoacoustic model for predicting wind turbine noise annoyance

Merino Martinez, R. (TU Delft Aircraft Noise and Climate Effects)
Pieren, Reto (Swiss Federal Laboratories for Materials Science and Technology (Empa))
Schäffer, Beat (Swiss Federal Laboratories for Materials Science and Technology (Empa))
Simons, D.G. (TU Delft Aircraft Noise and Climate Effects)

2022

Noise annoyance caused by wind turbines is a critical issue for the societal acceptance of wind energy. Wind turbine noise exposure is typically assessed using conventional time–averaged metrics, however, the literature suggests that these metrics do not fully capture the sound properties responsible for the perceived noise annoyance. Therefore, it is questionable to assess wind turbine noise and its abatement strategies using only such metrics. This paper presents a novel psychoacoustic model for predicting wind turbine noise annoyance that combines perception–based sound quality metrics. To establish the psychoacoustic model, the synthetic sound signals of two different wind turbines equipped with four state–of–the–art noise reduction add–ons (two types of trailing–edge serrations and two types of trailing–edge permeable materials inserts) were studied. Using a parametric wind turbine noise generator, the simulated sound signals were auralized at different observer positions and their noise annoyance was evaluated in two laboratory listening experiments with 16 and 10 subjects, respectively. The psychoacoustic annoyance model proposed here provides a very close agreement with the results of the listening experiment and improved accuracy compared to conventional sound metrics.

Wind turbine noise
Psychoacoustics
Noise annoyance

http://resolver.tudelft.nl/uuid:1eac1bf0-b595-4393-a994-053758e91006

Proceedings of the 24th International Congress on Acoustics

24th International Congress on Acoustics, 2022-10-24 → 2022-10-28, Gyeonju, Korea, Democratic People's Republic of

Institutional Repository

conference paper

© 2022 R. Merino Martinez, Reto Pieren, Beat Schäffer, D.G. Simons