- The study analyzed amplitude modulation (AM) of wind turbine noise recorded over 12 months near a wind farm in Finland. AM depth was highest in the 50-200 Hz frequency band. - AM occurrence depended on wind direction, with higher levels detected when wind blew from west and southwest, perpendicular and downwind of the microphone. AM frequency increased then dropped off with wind speed. - On average, wind turbine noise exhibited AM 24.95% of the time. While sound pressure levels were higher with increasing wind speed and in all directions, no significant correlation was found between wind speed/direction and AM.

1. Effect of Wind Speed and
Direction on Amplitude
Modulation of Wind
Turbine Noise
Thileepan Paulraj, University of Vaasa, Finland
Petri Välisuo, University of Vaasa, Finland
INTER-NOISE 2017
46TH INTERNATIONAL CONGRESS AND EXPOSITION
ON NOISE CONTROL ENGINEERING
HONG KONG
27 - 30 AUGUST, 2017

2. Summary
• Introduction
• Aim and Background of the research
• Methods
• Results

3. Amplitude
Modulation• Periodic fluctuations in the level of audible noise from one or more
wind turbines is defined as amplitude modulation (IOA, 2016)
• These fluctuations are perceived as ‘blade swish’ or ‘thumping’ noise
(Van den Berg, 2004) which occur at the blade passing frequency
(Renewable UK, 2013)
• ‘Swishing’ noise also known as Normal Amplitude Modulation
(NAM) is more audible closer (not more than 400-500 meters) to the
turbine. NAM is pronounced in cross-wind direction. (Renewable UK,
2013)
• Increase in the depth of AM compared to NAM, dominance of lower
frequencies in the spectrum and AM perception at large distances
from the turbine in upwind and downwind direction are
characteristics of Other Amplitude Modulation (OAM ). (Renewable
UK, 2013)

4. Cause for Amplitude
Modulation
• NAM is due to the directivity of the trailing edge noise and
practically all swishing noise is produced during the downward
movement of the blades. (Oerlemans & Schepers, 2009)
• Prime candidates for OAM source mechanism are local blade stall
and high levels of inflow turbulence. These factor could play an
important role in low frequency noise generation. (Renewable UK,
2013)

5. Effect of weather in
propagation of
amplitude modulated
WT noise• Larsson and Öhlund (2014) confirms that AM at immission point is
more common under certain metrological conditions. Wind direction
and sound speed gradient are identified to be crucial for AM
occurrence by the authors.
• Wind shear, lateral variation of wind speed and variation in the angle
of attack could be reasons for high levels of AM occurring at large
distances from the turbine. (Renewable UK, 2013)
• High frequencies of WT noise are attenuated in the atmosphere.

6. People are worried
• A survey conducted in Finland among residents living between 2.5
Km to 10 Km from 5 wind farms indicate that the residents are
concerned about the effect of WT noise in the soundscape and the
landscape also 19-22% of these residents think low frequency WT
noise is hazardous to health. (Turunen et al, 2016)
• Van den Berg (2004) has recorded the annoyance reported by a
resident living 1.5 km from a wind farm in the Germany-Netherlands
border.
• Results of a listening test conducted among 30 people indicate that
AM has a significant effect on annoyance and the annoyance
increases with the increase in AM depth. (Lee et al, 2011)

7. Current study
• Hence it becomes clear that weather affects both the source and the
propagation of amplitude modulated wind turbine noise and people
are annoyed by amplitude modulation.
• Through WindSoMe and WindCOE projects we are studying the
effects of weather on wind turbine noise around Kirkkokallio wind
farm in Honkajoki, Finland.
• Noise recordings started from 18.01.2016.
• The farm has 9 * 2.4 MW Nordex N117/2400 turbines.
• Noise data used in this paper were collected using a G.R.A.S 46AE
microphone at Risttilantie about 1.1 Km south to the nearest turbine
from the farm. Sampling rate was 25600 HZ.
• Weather data are measured using a SODAR equipment about 1 Km
to the North-East from the nearby turbine.
• AMWG’s method was used to identify AM and measure it’s depth.

8. Study location

9. Method
UK IOA (2016)
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10. Method
UK IOA (2016)
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11. Method
UK IOA (2016)
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12. Method
UK IOA (2016)
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13. Matching AM with
weather data
 The pass band 50 – 200 Hz
had the highest modulation
depth (3.19 dB) during the
entire analysis period
[1.4.2016 to 31.03.2017].
 SODAR records both the
wind speed and direction in
10 min averages.
 Time stamps of 10 minutes
modulation depth and 10
min SODAR data are
matched and only matched
data are used for further
analysis.

14. Results

15. Results

16. Results

17. Results

18. Results
Month Percentage of amplitude
modulated 10
minute periods in the frequency
band 50
– 200 Hz
April, 2016 8.02%
May, 2016 8.85%
June, 2016 27.04%
July, 2016 33.25%
August, 2016 33.15%
September,
2016
30.60%
October, 2016 34.60%
November, 2016 35.74%
December, 2016 45.49%
January,2017 32.36%
February,2017 32.36%
March, 2017 34.89%

19. Results
WS vs
SPL
WD vs
SPL
Pearson 0.28 -0.004
Spearman 0.32 -0.0017

20. Conclusions
• During the entire 12 months period between April, 2016 to March,
2017 the frequency band 50 – 200 Hz was the most amplitude
modulated in WT noise recorded at Kirkkokallio wind farm in
Finland.
• AM depth is dependent on wind direction but not on wind speed.
Higher levels of AM was detected when the wind blew from West
and South-West directions. At these times, the microphone was
located in cross-wind and down wind directions respectively.
• Frequency of occurrence of AM incidents increase with increase in
speed and drops sharply after a certain speed. The reason not known.
• According to our study, on average about 24.95% of the times the
wind noise was amplitude modulated.
• Higher SPLs were measured in all directions and more often at
higher wind speeds. So, we found no significant correlation between
WS vs SPL, WD vs SPL at times when AM was detected.

21. QUESTIONS