Class-Imbalanced Spatial-Temporal Feature Learning for Blade Icing Recognition of Wind Turbine

Blade icing detection is vital for wind turbines in cold climates, as it can prevent revenue loss and power degradation. Many machine learning models have been proposed to improve the detection of blade icing; however, earlier studies do not adequately address these issues due to the dynamics of sensor correlations and the imbalance of blade icing data, resulting in low precision and a high false alarm rate. In this study, we aim to address both of these challenges in order to identify blade icing more accurately. On this premise, we develop a spatial-temporal graph convolutional network (SGCN) that leverages the graph convolutional network for adaptively analyzing the dynamics of sensor correlations and a distance-based classifier to improve imbalanced learning. Experiments on the public UEA time series classification datasets and the real-world wind turbine datasets indicate that SGCN is capable of state-of-the-art accuracy, especially in the case of extremely imbalanced data.