Influence of atmospheric conditions on measured infrasound from wind turbines

Abstract

Scientific interest towards low frequencies and infrasound from wind turbines has renewed over the last decade as the size of wind turbines and the number of wind farms have continuously increased. However, few comprehensive studies are found assessing such noise emitted by wind turbines in response to meteorological parameters, including turbulent characteristics of the atmospheric boundary layer. The current work presents a detailed long-term measurement campaign combining infrasound recordings close to wind turbines and high-resolution meteorological parameters at three heights. An artificial neural network (ANN) model, developed based on the measurement data, was able to adequately predict both blade passing frequency levels and broadband infrasonic noise. The model allowed discriminating between the broadband inflow noise emitted by the wind turbines and the remaining microphone-induced wind noise under the wind-shielding dome for a wide range of atmospheric conditions. At the same time, the ANN allowed deriving physically plausible relationships between meteorological and turbulence parameters and wind turbine emitted infrasound components. Dominant predictors for the infrasound production were mean wind speed, turbulence intensity and turbulent vertical heat flux.