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

• Published in: Energy (Oxford) 2024-10, Vol.305, p.132257, Article 132257
• Main Authors: Cheng, Biyi, Yao, Yingxue, Qu, Xiaobin, Zhou, Zhiming, Wei, Jionghui, Liang, Ertang, Zhang, Chengcheng, Kang, Hanwen, Wang, Hongjun
• Format: Article
• Language: English
• Subjects: Data-driven surrogated model; Deep learning algorithm; Large-scale offshore wind turbine tower; Multi-objective parameter optimization
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.132257

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-15-240 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. •An AI-model assisted NSGA-II framework is proposed.•Optimization functions were established by learning model CNN, BPNN, and SVM.•The prediction performances of CNN, BPNN, and SVM are compared and analyzed.•The tower mass and top deflection of IEA-15-240 and IEA-22-240 are minimizing.•CNN can handle large size of dataset with 62 inputs and 2 outputs.