Multi-objective parameter optimization of large-scale offshore wind Turbine's tower based on data-driven model with deep learning and machine learning methods

The tower plays a crucial role in wind turbine systems. However, the design and optimization of configuration parameters have traditionally been lacking in intelligent methods. This study proposes a multi-objective parameter optimization framework that incorporates artificial intelligence models. Specifically, the diameters and thicknesses of the tower are the design parameters that strongly influence two conflicting optimization objectives: mass and top deflection. The nonlinear relationship between these parameters is predicted using surrogate models, such as the Convolutional Neural Network (CNN), Back-propagation Neural Network (BPNN), and Support Vector Machine (SVM), which serve as optimization functions. Additionally, the solutions must meet the requirements for frequency, stress, and buckling. In this study, two reference wind turbines, namely, IEA-15240 and IEA-22-280, are selected as case studies, and the open-source software WISDEM is utilized to construct the training and testing datasets. Bayesian optimization is used to fine-tune the hyperparameters. Results show that the CNN model outperforms others with larger datasets. Leveraging Deep Learning in the design of offshore wind turbines can significantly reduce mass and deflection while maintaining integrity and performance.