Parameter Optimization of floating foundations for Offshore Wind Turbines Based on Machine Learning

With the increasing global demand for renewable energy, offshore wind power generation has attracted great attention, especially the development of floating offshore wind turbines .Offshore floating wind turbine foundations involve multiple key dimensional parameters that strongly interact and influence the system's extreme motion response. Traditional optimization methods struggle to handle these complex couplings, necessitating AI-driven approaches like neural networks for intelligent parameter optimization. However, limited dataset availability in this emerging field poses a challenge. This paper proposes a small-sample-based optimization method for wind turbine foundation design. The HexaSemi-submersible FOWT was optimized by tuning four key parameters :column spacing (L), platform draft (T), column diameter (D), and caisson height (h). OpenFAST simulated motion responses, followed by a BP neural network modeling parameter-displacement relationships. Coupled with genetic algorithms, this approach reduced platform displacement by 16.03% versus the original design configuration.