Wind Turbine Blade Breakage Monitoring With Deep Autoencoders

Abstract

Monitoring wind turbine blade breakages based on supervisory control and data acquisition (SCADA) data is investigated in this research. A preliminary data analysis is performed to demonstrate that existing SCADA features are unable to present irregular patterns prior to occurrences of blade breakages. A deep autoencoder (DA) model is introduced to derive an indicator of impending blade breakages, the reconstruction error (RE), from SCADA data. The DA model is a neural network of multiple hidden layers organized symmetrically. In training DA models, the restricted Boltzmann machine is applied to initialize weights and biases. The back-propagation method is subsequently employed to further optimize the network structure. Through examining SCADA data, we observe that the trend of RE will shift by the blade breakage. To effectively detect RE shifts through online monitoring, the exponentially weighted moving average control chart is deployed. The effectiveness of the proposed monitoring approach is validated by blade breakage cases collected from wind farms located in China. The computational results prove the capability of the proposed monitoring approach in identifying impending blade breakages.