Research on Wind Turbine Fault Diagnosis Method Realized by Vibration Monitoring

Abstract

Wind energy is one of the fast evolving renewable energy sources that has seen widespread application. Therefore, research on its carrier, the wind turbine, is growing, and the majority of them concentrate on the diagnosis of wind turbine faults. In this paper, the vibration signals collected in the time domain by vibration monitoring were analyzed, and the fault characteristic parameters were identified. These parameters were then inputted into a genetic algorithm back-propagation neural network (GA-BPNN) for wind turbine fault diagnosis. It was found that the presence of defects in the wind turbine depended on the effective value, peak value, and kurtosis of the vibration signal. The overall recognition accuracy of the GA-BPNN was 94.89%, which was much higher than that of the support vector machine (88.7%) and random forest (88.35%). Therefore, it is feasible and highly accurate to extract fault characteristic parameters through vibration monitoring and input them into a GA-BPNN for wind turbine fault diagnosis.