Graph Spatio-Temporal Networks for Condition Monitoring of Wind Turbine

Condition monitoring of wind turbines (WTs) is essential for advancing wind energy. Existing data-driven methods heavily rely on deep learning and Big Data, leading to challenges in distinguishing true faults from false alarms, impacting operational decisions negatively. Thus, this paper proposes a spatio-temporal graph neural network framework that incorporates prior knowledge. Prior WT knowledge is utilized by establishing a spatially structured directed graph embedded in a graph attention network (GAT). The features in WTs' supervisory control and data acquisition system are indicated by the nodes in GAT. Then, the global and local attention embedding layers as well as long short-term memory layers are employed to combine spatio-temporal information from each node. Finally, the condition monitoring in WTs' graph and node-level are established, and a fault propagation chain at node-level is constructed for explaining condition monitoring results. To demonstrate the explainability, robustness and sensitivity of the proposed approach, a comparative analysis between a true fault case and a false alarm case are given, and anomaly detection results are also reported.