KNN fault state monitoring of wind turbine based on SCADA system
In order to further improve the monitoring accuracy of the fault state of the wind turbine equipment,the data acquisition and monitoring control(SCADA)system of the 2 MW wind turbine is used for data acquisi-tion and testing.The K-nearest neighbor(KNN)algorithm is used to comprehensively evaluate the parameters of all working conditions under the fault state of the wind turbine.The abnormal rate is obtained by the method of statistical process control SPC and sliding window,and the actual running state of the gear box is monitored in re-al time.The results show that the optimized distance measurement can greatly improve the prediction accuracy.The outlier clipping training set also loses a certain proportion of effective training samples,which improves the operation efficiency.Setting the similar clip threshold can improve the prediction accuracy by 0.62%,reduce the accuracy by 2.48%and increase the operation efficiency by 20.92%after two clips.
wind turbinegearboxcondition monitoringKNN methodsupervisory control and data acquisition
万方数据
维普
