More wind turbines could mean less sleep for locals

Diurnal and seasonal variation of AM characteristics. Credit: Flinders University

Flinders University experts are using machine learning and other signal processing techniques to characterise annoying noise features from wind farms.

The University team has released two new publications from the ongoing Wind Farm Noise Study. The research finds that ‘swoosh’ sounds from wind turbines are likely to be heard by neighbouring residents up to five times more often during the night than during daylight hours, depending on wind direction, season and wind farm distance.

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The ‘swoosh’ sounds are technically referred to as amplitude modulation (AM) and research has combined long-term monitoring of wind farm noise with machine learning, as well as available knowledge to quantify and characterise AM in wind turbine noise.

The research was led by Flinders University PhD candidate Duc Phuc Nguyen and acoustic expert Dr Kristy Hansen.

Dr Kristy Hansen and Mr Phuc Nguyen at the Adelaide Institute for Sleep Health. Credit: Flinders University.

“We found that the amount of amplitude modulation present during the daytime versus night-time varies substantially occurring two to five times more often during the night-time compared to the daytime,” says Nguyen.

“The noise seems to worsen after sunset when amplitude modulation can be detected for up to 60% of the night-time at distances around 1km from a wind farm.

“At greater than 3km, amplitude modulation also occurs for up to 30% of the night-time.”

The Wind Farm Noise Study, based at the Adelaide Institute for Sleep Health at Flinders University, is investigating noise characteristics and sleep disturbances at residences located near wind farms.

The association between wind turbine noise and adverse effects on humans is an ongoing debate, however, Dr Hansen says the directional nature of wind turbine noise means residents living in downwind and crosswind conditions are likely to be more disturbed by wind turbines.

“We found that AM occurs most often during these wind directions,” she says. “Using these recent advances in machine learning, we have been able to develop an AM detection method that has a predictive power close to the practical limit set by a human listener.

“This includes the noise that increases and decreases as the blades rotate, or AM, including a ‘swoosh’ sound, which further contributes to the negative effects of wind turbine noise.”

Read more about the study.

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