Optimization and early warning strategy for wind turbine blade acoustic signature monitoring array based on wind farm simulation
Complex wind farm environments cause severe spatial aliasing and signal attenuation in blade acoustic signature monitoring. This paper presents an acoustic sensor array topology optimization method based on multi-physics simulation for high-fidelity acquisition of weak voiceprint features. A three-dimensional sound field model coupling aerodynamic noise and mechanical vibration quantifies sound propagation under varying wind speeds and yaw angles. A heuristic particle swarm algorithm discretely optimizes microphone array coordinates on tower and nacelle surfaces by maximizing the signal-to-noise ratio. A surrogate model accelerates sound field evaluation while eliminating nodes disturbed by strong wind vortices. Experiments on a public blade crack acoustic dataset show that the optimized array reduces normalized root mean square error by 44.4 percent compared to conventional spiral arrays, and the proposed attention-based multi-scale network achieves an area under the curve of 0.967 and recall of 0.913.