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Characterisation of noise in homes affected by wind turbine noise

SUMMARY AND CONCLUSION

This paper has described a new methodology for recording noise and annoyance within residents’ homes affected by wind turbine noise. The technique records time-series recordings that allows complete analysis of the signal using a variety of post processing techniques. While being used to characterise wind turbine noise in this study, the system can be used to record noise and annoyance in residents’ homes affected by other forms of environmental noise.

Measurements taken in a single resident’s home near a wind farm show an increase in the overall mean Z (unweighted) and C weighted sound level with Annoyance rating. No increase was, however, observed in the mean A weighted sound level and this is due to the majority of the acoustic energy being contained in the lower frequencies. In particular, the energy levels within the 10-30 Hz band were observed to increase with Annoyance rating. Additionally, significant amplitude modulation was detected in the noise signals; however, no trend with annoyance was observed.

It should be noted that the results presented in this paper are the preliminary results of a much larger study to investigate the character of wind turbine noise within homes. There is a need for a much more comprehensive data set measured in a large number of homes to draw more definite conclusions about the nature of noise in residences close to wind farms.

Future measurements with the system will incorporate use of a microphone capable of measuring below 1 Hz to capture noise over a larger frequency range than is reported in this study. Additionally, it is hoped that wind farm operational data can be obtained to correlate power production, wind condition and rotor motion with residents’ noise measurements. Another improvement is the incorporation of a high-resolution data acquisition system that will eliminate the need for an amplifier. A weather station located near the home would also be beneficial to record local meteorological conditions that will help identify wind noise from foliage and building facades.

Benjamin Nobbs, Con J. Doolan and Danielle J. Moreau
School of Mechanical Engineering, The University of Adelaide, Adelaide, Australia

Proceedings of Acoustics 2012—Fremantle, 21-23 November 2012, paper peer reviewed

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