Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning

文献类型： 外文期刊

第一作者： Zhao, Xudong

作者： Zhao, Xudong;Ma, Qingfen;Li, Jingru;Wu, Zhongye;Xiong, Yang;Lu, Hui

作者机构：

关键词： dynamic submarine cable; BP neural network; optimization algorithm; tornado optimization (TOC); offshore wind power; lazy wave

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）

ISSN：

年卷期： 2025 年 13 卷 5 期

页码：

收录情况： SCI

摘要： The safe and efficient design of dynamic submarine cables is critical for the reliability of floating offshore wind turbines, yet traditional time-domain simulation-based optimization approaches are computationally intensive and time consuming. To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. A high-fidelity surrogate model based on a backpropagation (BP) neural network was trained to accurately predict cable dynamic responses. Three optimization algorithms-Particle Swarm Optimization (PSO), Ivy Optimization (IVY), and Tornado Optimization (TOC)-were evaluated for their effectiveness in optimizing the arrangement of buoyancy and weight blocks. The TOC algorithm exhibited superior accuracy and convergence stability. Optimization results show an 18.3% reduction in maximum curvature while maintaining allowable effective tension limits. This approach significantly enhances optimization efficiency and provides a viable strategy for the intelligent design of dynamic cable systems. Future work will incorporate platform motions induced by wind turbine operation and explore multi-objective optimization schemes to further improve cable performance.

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