A novel method to predict the stiffness evolution of in-service wind turbine blades based on deep learning models

Abstract

Since wind turbines operate in a complex environment for long term, the fatigue behavior of the blades can be influenced by wind, illumination, moisture, temperature, and so forth. For wind turbine blade manufacturers, the determination of their fatigue limit before delivery is necessary and fatigue acceleration experiments usually require a lot of labor and experimental costs. As a machine learning paradigm, deep learning focuses on the inherent hierarchical models of data and has achieved notable success in computer vision, speech recognition, natural language processing, etc. Aimed at reducing the time and the costs during fatigue tests, this paper studies a training-based method for wind turbine blade stiffness prediction using time series stiffness data under fatigue tests. Based on deep learning methods including convolutional neural network, long-short term memory network and the hybrid network, the residual stiffness of the blade with fatigue life under fatigue tests is obtained by combining the fatigue historical data. The obtained results show that the developed models can learn features directly from raw stiffness data and complete the residual stiffness prediction in succession. White Gaussian noise with different signal-to-noise ratios is also added to all stiffness data to demonstrate the models’ feasibility of stiffness prediction.