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This article focuses on data-driven approaches for icing detection (ID) on wind turbine blades. In light of the widespread application of sensor technologies in wind turbines, such data-driven ID methods have become increasingly prominent. However, current methods have deficiencies, particularly in acknowledging the structural properties of multivariate sensor data and in differentiating icing stages, both critical for the identification of failure patterns. To bridge these gaps, we propose a spatiotemporal attention Siamese network (STASN) for blade ID. This model employs a Siamese network architecture for efficient few-shot learning amidst class imbalance. It uniquely incorporates a graph attention network and gated recurrent unit for extracting spatiotemporal features from sensor data. This design not only acknowledges the spatial structure of the data but also distinctly identifies features pertinent to various icing stages. The efficacy of STASN was validated using actual sensor data from supervisory control and data acquisition systems. The results demonstrate STASN's capability in discerning distinct icing stage features and its potential in early icing prediction. This research underscores STASN's utility in providing advanced, flexible fault alarms for blade icing, representing a significant stride in wind turbine maintenance and safety.
Wang et al. (Wed,) studied this question.