Wind turbine noise measurement using a compact microphone array with advanced deconvolution algorithms

This paper experimentally investigates the noise from a large wind turbine (GE 1.5 MW) with a compact microphone array (OptiNav 24) using advanced deconvolution based beamforming methods, such as DAMAS and CLEAN-SC beamforming algorithms, for data reduction. Our study focuses on the ability of a compact microphone array to successfully locate both mechanical and aerodynamic noise sources on the wind turbine. Several interesting results have emerged from this study: (i) A compact microphone array is sufficient to perform a detailed study on wind turbine noise if advanced deconvolution methods are applied. (ii) Noise sources on the blade and on the nacelle can clearly be separated. (iii) Noise of the blades is dominated by trailing edge noise which is frequency dependent and is distributed along the length of the blade with the dominant noise source closer to the tip of the blade. (iv) The LP and DAMAS algorithms represent the distributed trailing edge noise source better than CLEAN-SC and classical beamforming. (v) Additional tonal noise produced during yawing operation is believed to be radiating from the tower of the wind turbine that acts like a resonator. (vi) Ground reflection is not believed to have a significant effect on noise source location estimates in this study.