Taking wind turbine grid-side converter voltage faults as the research target,by analyzing the features of SCADA system data,the conventional data complementation and deletion processes for missing data,outlier data,and discrete abnormal data were conducted on priori knowledge,and the unrecognizable stacked data were cleaned by the least squares-based variable point grouping method,and fault characteristic variables for converter faults in wind turbines were selected with empirical identification method.The TCN deep learning network algorithm was applied to analyze the wind turbine converter SCADA data with temporal characteristics,and the fault prediction was performed based on the fault feature identification,and the prediction accuracy reached 96.56％.
wind turbinegrid-side converter voltage failurefault predictionTCN
NETL
NSTL
万方数据
