Condition Monitoring-Oriented Wind Turbine Early Fault Rule K-Nearest Neighbor Matching Method

Abstract

Due to the lack of data cleaning links in traditional methods, there are many false alarms and the problem of susceptibility to noise during the monitoring process. For this reason, this paper proposes a condition monitoring-oriented wind turbine early fault rule k nearest neighbor matching method. Obtain static and dynamic parameters through identification of wind turbine operating state parameters, use nuclear density estimation repair method to repair missing data, use rule k nearest neighbor matching method to remove abnormal data and noise data, and use ReliefF algorithm to screen wind turbine operating faults based on data processing results feature. Finally, the uncertainty of the fault status of the wind turbine is analyzed, and the early fault monitoring platform of the wind turbine is established based on the analysis result to realize the early fault monitoring of the wind turbine. Experimental results show that the method has better anti-noise performance and lower fault false alarm rate.