Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy

To improve the efficiency and reliability of wind power generation, condition monitoring of wind turbines has drawn extensive attention worldwide. However, blade health monitoring is still challenging because of volatile operating conditions and the dependence on the assumption that healthy and unhealthy measurements can be naturally separated after the training stage. In this paper, a self-supervised health representation learning method is proposed to address these problems, and only healthy measurements are required in training. Specifically, data representations related to blade health conditions are learned by neural networks though data augmentation and an auxiliary task. In this case, the interference of operating circumstances and noise can be eliminated, and the volatility of measurements can be suppressed to establish accurate models of healthy operations. Moreover, the separability assumption is guaranteed by imposing constraints on the representation distributions of unhealthy samples, improving the reliability of decision making based on the learned knowledge. Blade health conditions are recognized using kernel density estimation. The satisfactory performance of the proposed method is demonstrated through laboratory and field measurements, achieving higher accuracy than existing approaches for online health monitoring. This work contributes to the economy of clean energy utilization.