An Unsupervised Approach to Wind Turbine Blade Icing Detection Based on Beta Variational Graph Attention Autoencoder

Supervised deep-learning methods are data-driven and widely used for wind turbine blade icing detection (ID). Data driven methods generally require a complete dictionary of labeled sensor data. However, labeling sensor data increases engineering costs and can introduce costly errors such as incorrect data labels. Additionally, the reported data-driven approaches ignore the contribution of structural properties of multivariate sensor data to failure patterns identification. To address these shortcomings, the current study proposes a Beta variational graph attention autoencoder (β-VGATAE) for blade ID. The β-VGATAE model employs a Beta variational autoencoder (β-VAE) architecture to achieve unsupervised learning. A graph attention network is used as a spatial feature extractor within the β-VAE architecture since it considers the spatial structure of the sensor data. Actual sensor data from supervisory control and data acquisition systems were used to validate the proposed model. Specifically, we verified the rationality of designing each component in the β-VGATAE. Experimental results show that the highest levels of accuracy achieved were 90.9% and 93.4% for the respective scenarios involving two wind turbines; the β-VGATAE detection model has high accuracy and excellent generalization ability.