Abstract
Efficient wind power cluster management and sustainable industry development require an optimized maintenance approach integrating condition monitoring. However, the intricate spatiotemporal correlations and non-stationarity of the data continue to pose significant challenges in extracting valuable insights from the supervisory control and data acquisition (SCADA) system. Hence, this paper proposes a novel approach for Dynamically Fusing Spatio-Temporal information with Difference Excitation (DFST-DE) in wind power systems’ condition monitoring. First, it extracts spatial dependencies by integrating sparse graph structures with Gumbel Softmax regularization and incorporates temporal information using a hybrid adaptive self-attention mechanism. This combined approach effectively captures comprehensive spatiotemporal correlations. Second, it proposes a Spatio-Temporal Difference Excitation module to mitigate non-stationarity in time series data by smoothing trend changes. The proposed DFST-DE approach achieves superior performance by effectively fusing spatial, temporal and excitation information. Finally, it optimizes anomaly detection accuracy and efficiency by dynamically adjusting the threshold based on anomaly score statistics. Experimental results on real-world wind farm datasets demonstrate that the proposed approach outperforms other established methods in detecting early abnormal conditions in wind turbines.
Published Version
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