Quantifying the Acoustic Bias of Insect Noise on Wind Turbine Sound Power Levels at Low Wind Speeds

Accurate wind turbine noise (WTN) measurements are essential for environmental compliance and noise impact assessments. However, these measurements are often polluted by background biological noise, especially from insects. Insect noise is typically assumed to be irrelevant due to frequency separation. This study challenges this assumption by demonstrating that insect sounds, specifically those of the cricket Oecanthus pellucens, can overlap with turbine noise in the 2.5 kHz band and introduce significant measurement bias at low wind speeds. The featured application is a machine learning-based methodology to filter confounding biological sounds (e.g., insect calls) from wind turbine noise measurements. By correcting for these acoustic contaminants, which typically lead to an overestimation of turbine noise at low wind speeds, the method enables more accurate environmental noise impact assessments. This directly supports the development of evidence-based regulatory policies and guidelines. Using long-term acoustic monitoring and an unsupervised Gaussian Mixture Model (GMM) clustering approach, we classified and excluded insect noise from recorded data. We found that the presence of cricket calls can increase measured wind turbine sound power levels (WTSPL) by more than 3 dBA at wind speeds below 6 m/s, with peak deviations reaching up to 10 dBA. These findings have significant implications for rural or low-wind regions where turbine operation at partial load is frequent. Our results underscore the importance of insect noise filtering when performing WTN assessments to ensure regulatory accuracy, particularly when long-term average noise modeling is used for compliance. The presented methodology provides a robust framework for distinguishing insect noise and can improve the consistency and credibility of WTN measurements under real-world environmental conditions.