Deep neural network-based wind speed forecasting and fatigue analysis of a large composite wind turbine blade

The purpose of this paper is to analyze the modern deep neural networks such as nonlinear autoregressive network with external inputs and a recurrent neural network called long short-term memory for wind speed forecast for long-term and use the prediction for fatigue analysis of a large 5 MW wind turbine blade made of composite materials. The use of machine learning algorithms of advanced neural network applied for engineering problems is increasing recently. The present paper therefore brings as important connection between these latest machine learning methods and engineering analysis of complex wind turbine blades which are also the focus of researchers in renewable system design and analysis. First, a nonlinear autoregressive network with external inputs neural network model using Levenberg-Marquardt back propagation feed forward algorithm is developed with 5 years of environment parameters as input. Similarly, a long short-term memory based model is developed and compared. The chosen long short-term memory model is used for developing two-year wind speed forecast. This wind pattern is used to create time varying loads on blade sections and cross-verified with National Renewable Energy Laboratory tools. A high-fidelity CAD model of the NREL 5 MW blade is developed and the fatigue analysis of the blade is carried out using the stress life approach with load ratio based on cohesive zone modeling. The blade is found to have available life of about 23.6 years. Thus, an integrated methodology is developed involving high-fidelity modeling of the composite material blade, wind speed forecasting using multiple environmental parameters using latest deep learning methods for machine learning, dynamic wind load calculation, and fatigue analysis for National Renewable Energy Laboratory blade.