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Condition monitoring of wind turbines is critical for increasing the reliability of the turbines and reducing their operation and maintenance costs. Supervisory control and data acquisition (SCADA) systems have been widely regarded as a promising technique to monitor the health status of turbines due to their abundance and cost-effective operation data. However, SCADA data are fundamentally multivariate time series with inherent spatio-temporal correlations. Therefore, it is still difficult to extract such correlations and then accurately identify the health status. This paper proposes a novel multi-view spatio-temporal feature fusion approach (MVSTCNN) based on convolutional neural networks (CNN) for condition monitoring of wind turbines. Specifically, multiple CNN modules with convolutional kernels of varying sizes are designed to extract correlations among several sensor variables and the temporal dependency concealed in each variable in parallel. A main advantage of the proposed method is its capacity to capture multiscale local information and global information simultaneously in both temporal and spatial dimensions, which improves the performance of condition monitoring. Real SCADA data from a wind farm is utilized to evaluate the effectiveness and superiority of the proposed approach. The result demonstrates that the proposed approach is effective for early fault detection in wind turbines.

A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data