A novel fault diagnosis method for wind turbine based on adaptive multivariate time-series convolutional network using SCADA data

Actual wind farms usually perform condition monitoring of wind turbines based on simple trigger logic, which often have high false alarm rates and overlapping warnings. Supervisory control and data acquisition (SCADA) data contain operational information of wind turbines, based on which automatic fault diagnosis is extremely valuable. However, SCADA data have complex characteristics such as high-dimensional variable correlation, dynamic variability, and temporal correlation which bring challenges to fault diagnosis. Therefore, this paper proposes a novel fault diagnosis method to automatically identify the operating state of wind turbines: adaptive multivariate time-series convolutional network (AdaMTCN). AdaMTCN resamples SCADA data with multiple time steps at first, and the obtained resampling matrices contain multivariate time series information of different states. Then, multivariate time series convolutional networks (MTCN) are innovatively proposed, which extract enriched features from the multiple time step resampling matrices. Finally, according to the proposed adaptive decision fusion method, multiple MTCN models are weighted and fused into the overall AdaMTCN. Experimental results based on real case show that the AdaMTCN has excellent diagnostic performance in the face of SCADA data with complex characteristics.