SCADA Data-Driven Spatio-Temporal Graph Convolutional Neural Network for Wind Turbine Fault Diagnosis

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 10
• Main Authors: Ma, Jiachen, Fu, Yang, Cheng, Tianle, He, Deqiang, Cao, Hongrui, Yu, Bin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Control systems; Correlation; Data mining; Deep learning; Fault diagnosis; Feature extraction; graph neural network; Graph neural networks; intelligent fault diagnosis; Kernel; Kernel functions; Machine learning; Neural networks; Sandwich structures; Sensor phenomena and characterization; spatio-temporal fusion; Spatiotemporal data; Supervisory control and data acquisition; Turbines; Wind speed; wind turbine; Wind turbines
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2025.3551875

Supervisory control and data acquisition (SCADA) systems collect vast amounts of multi-sensor monitoring data, which is widely used in the intelligent fault diagnosis of wind turbines with the fast development of deep learning technologies. However, the complex structure of wind turbines and their time-varying operating conditions result in intricate spatio-temporal correlations within SCADA data, presenting significant challenges for feature extraction and accurate fault diagnosis. Current spatio-temporal fusion methods often treat SCADA data as Euclidean data, limiting their ability to capture the complex spatio-temporal coupling characteristics, which leads to reduced diagnostic accuracy. To solve abovementioned problems, a novel deep learning-based spatio-temporal graph convolutional neural network (STGCN) is developed for intelligent fault diagnosis of wind turbines in this article. First, an adjacency matrix is constructed based on the Gaussian kernel function to graphically represent the SCADA data, so as to improve the representation capacities for spatial characteristics. Then, the spatial and temporal fault features are extracted using the graph convolutional network (GCN) and the 1-D convolutional network (1D-CNN), respectively. Finally, a spatio-temporal feature fusion module is developed as the sandwich structure to construct the proposed STGCN. The feasibility and effectiveness of the proposed method are verified by two cases of blade icing detection and main bearing wear diagnosis. The results show that the proposed method is able to accurately describe the spatio-temporal correlation of SCADA data, and improve the diagnostic accuracy.