High Generalization in Anomaly Detection of Wind Turbine Generator Based on Voiceprint

Abstract

The major research work of the paper is the anomaly diagnosis of wind turbine generator based on voiceprint. The research finds that the reference and comparison between the three blades of a single wind turbine can diagnose whether the wind turbine is faulty. On this base, the paper proposes a periodic audio cutting method based on clustering and median convergence, which effectively cuts the voiceprint, reduces the amount of calculation, and provides a basis for subsequent anomaly detection. The steady-state difference method between three blades of wind turbine is used to detect anomalies, which avoids the migration failure of algorithm caused by the changes of channels and objects to be checked. The paper provides an effective technical means for fan blade inspection.