Research on Fault Diagnosis of Hydraulic Wind Turbine Based on DBSCAN-ML
Traditional wind turbines are limited and predetermined for system solutions of faults,but fault prediction diagnosis with a large amount of sensor data can effectively prevent possible system faults,thus reducing equipment maintenance costs.Therefore,a fault diagnosis strategy for wind turbines based on DBSCAN-ML was proposed.The density-based spatial clustering of applications with noise(DBSCAN)was applied to classify wind turbine data in abnormal state from normal state data,and then two machine learning(ML)algorithms,namely decision tree and random forest algorithm,were used to construct prediction models.Finally,K-fold cross vali-dation was used to test.With the data of 31 wind turbines in Guangxi,this fault diagnosis scheme was verified by case.The results show that DBSCAN algorithm can effectively separate abnormal state data,and the accuracy of decision tree prediction model and random for-est model can obtain 92.7％and 92.1％,respectively.Through data mining and modeling,wind turbine faults can be detected.The main-tenance needs of the parts can be predicted.
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