Alexander Sutin -asutin@stevens.edu
Hady Salloum – hsalloum@stevens.edu
Alexander Sedunov- asedunov@steves.edu
Nikolay Sedunov – nsednov@stevens.edu
Stevens Institute of Technology
Sensor Technologies & Applied Research (STAR) Center
Hoboken, NJ 07030
Popular version of 2aNS7 – Directional Processing in Assessment of Wind Turbine Noise
Presented Tuesday morning, May25, 2022, 10:50-11:05 AM, Mountain
182nd ASA Meeting, Denver
Read the article in Proceedings of Meetings on Acoustics
Assessments of Wind Turbine Generator (WTG) noise are required to comply with the US Environmental Agency and local governments and avoid legal action that may result of non-compliant operation. Current methods for WTG noise measurements require the comparison of long-term sound data recorded before and after a WTG installation. These measurements must be conducted during several months for various wind speeds and environmental conditions.
The acoustic measurements conducted for a working WTG are not reliable due to the contamination of the measurements by sources other than the noise from the wind turbines[1]. Such sources of noise include traffic (highway, rail and air), construction, industrial facilities, wind in the trees, social activities, animals, birds , etc.
The goal of our paper is to provide suggestions on how the use of a microphone array could improve the WTG noise assessment by two ways: (1) identifying and attributing noise contribution to specific sources (2) by emphasizing of acoustic signal from the WTG.
As an example of the microphone array, we consider the sensors developed at Stevens Institute of Technology [2], [3] for low-flying aircraft and drone detection (see Figures 1a and b), these arrays have between 5 and 10 microphones.
These sensors use a signal processing algorithm based on the correlation between the signals received by the elements of the array to find direction towards sound sources and beamforming to emphasize the acoustic signal coming from specific directions.
As a result, it is possible to identify sounds not originating from the wind turbine and remove the affected time frames from the averaged measurement of noise levels. The Stevens array directivity (see Figure 1c) shows enhancing of the signal using beamforining.
LFADSystem
DARAPicture
ARADirectivityPattern
Figure 1: Examples of acoustic arrays capable of direction-finding: a – acoustic system for low flying aircraft detection [2], b –array for unmanned aerial vehicle detection,c – the beam pattern for the latter array shown as relative gain depending on steered direction and frequency.
Previous prolonged deployments have provided examples of noise observation and angular localization from various sources. Figure 3 displays the spectrogram and signal angular output showing a complex situation with passing trains and vehicles.
Figure 2. An example of SRP-PHAT processing shows a complex situation with noise from a cargo train (T) and vehicles (V).
The configuration of the current Stevens system was optimized for low flying aircraft and unmanned aerial vehicle detection and localization. Since the low-frequency noise components from wind turbines are a concern for the WTG assessment, the placement of the micropnes in the arry arrays can be modified to operate in the appropriate frequency band.
References
[1] S. Cooper and C. Chan, “Determination of Acoustic Compliance of Wind Farms,” Acoustics, vol. 2, no. 2, pp. 416–450, 2020.
[2] A. Sedunov, A. Sutin, N. Sedunov, H. Salloum, A. Yakubovskiy, and D. Masters, “Passive acoustic system for tracking low-flying aircraft,” IET Radar, Sonar Navig., vol. 10, no. 9, pp. 1561–1568, 2016.
[3] A. Sedunov, D. Haddad, H. Salloum, A. Sutin, N. Sedunov. and A. Yakubovskiy, A., “Stevens drone detection acoustic system and experiments in acoustics UAV tracking.” In 2019 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-7). IEEE.