Cross-turbine fault diagnosis for wind turbines with SCADA data: A spatio-temporal graph network with multi-task learning

Supervisory Control And Data Acquisition (SCADA) data based fault diagnosis for wind turbines is gaining attention due to its accessibility and affordability. However, existing deep learning methods struggle to effectively model SCADA data because of its complex spatio-temporal correlations. In addition, both the variability among wind turbines and the poor generalization of models make it difficult to port the trained model with existing data to others. To tackle these issues, a spatio-temporal graph convolutional network with multi-task learning named MTSTGCN has been proposed for cross-turbine fault diagnosis. MTSTGCN includes a graph data construction module, a graph learning module to learn spatio-temporal features, a domain adaptive module to learn invariant features for different turbine domains and a deep metric learning module for feature discrimination enhancement. The model is trained end-to-end, enabling collaborative multi-task training. The results of experiments conducted on two SCADA datasets indicate that the proposed method outperforms other baseline approaches.