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

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.