SGG-DGCN: Wind Turbine Anomaly Identification by Using Deep Graph Convolutional Networks with Similarity Graph Generation Strategy

Abstract

In order to minimize wind turbine failures, fault diagnosis of wind turbines is becoming increasingly important, deep learning methods excel at multivariate monitoring and data modeling, but they are often limited to Euclidean space and struggle to capture the complex coupling between wind turbine sensors. To address this problem, we convert SCADA data into graph data, where sensors act as nodes and their topological connections act as edges, to represent these complex relationships more efficiently. Specifically, a wind turbine anomaly identification method based on deep graph convolutional neural network using similarity graph generation strategy (SGG-DGCN) is proposed. Firstly, a plurality of similarity graphs containing similarity information between nodes are generated by different distance metrics. Then, the generated similarity graphs are fused using the proposed similarity graph generation strategy. Finally, the fused similarity graphs are fed into the DGCN model for anomaly identification. To verify the effectiveness of the proposed SGG-DGCN model, we conducted a large number of experiments. The experimental results show that the proposed SGG-DGCN model has the highest accuracy compared with other models. In addition, the results of ablation experiment also demonstrate that the proposed SGG strategy can effectively improve the accuracy of WT anomaly identification. Conflict of Interest Statement The authors declare no conflicts of interest.