Dominant Frequency Extraction for Operational Underwater Sound of Offshore Wind Turbines Using Adaptive Stochastic Resonance

Abstract

Underwater sound generated by the rapidly increasing offshore wind farms worldwide greatly affects the underwater soundscape and may cause long-term cumulative effects on sound-sensitive marine organisms. However, its analysis and impact assessment are heavily interfered with by underwater ambient noise. In this study, an adaptive stochastic resonance method is proposed to extract the dominant frequency of wind turbine operational sound when heavy noise is present. In particular, a time–frequency–amplitude fusion index was proposed to guide the parameter tuning of an adaptive stochastic resonance system, and an equilibrium optimizer based on the physical dynamic source–sink principle was adopted to optimize the parameter-tuning process. The results from the simulation and field data showed that the dominant frequency of operational sound was extracted adaptively. For field data with wind speeds of 4.13–6.15 m/s (at 90 m hub height), the extracted dominant frequency varied with wind speed between 90 and 107 Hz, and it was highly correlated with the wind turbine rotor speed monitored synchronously in the air, with a correlation coefficient of 0.985. Compared to other existing methods, our method has a higher output signal-to-noise ratio and a shorter running time.