Optimization of mooring systems for a 10MW semisubmersible offshore wind turbines based on neural network.

The gradual transition of wind power from onshore to deep sea poses an urgent challenge in optimizing cost-efficiency while ensuring the safety of floating wind turbines. This paper presents a new approach for parametrically optimizing the mooring system for offshore floating wind turbines. A semi-submersible wind turbine platform was selected as the reference structure, with a calculated water depth of 130 m that meets the requirements of the application of the wind turbine platform. A sample library of mooring system parameters was established and the wind-wave coupling simulations were carried out using OpenFAST. Considering platform safety as a priority, a combination of BP neural network and genetic algorithm was employed to optimize mooring system parameters to minimize the costs. This approach provides an important foundation for future research on the parameter optimization of mooring systems for floating wind turbines. • A novel methodology to optimize the mooring system for a semi-submersible floating wind turbine is presented. • The relationship between the mooring parameters and the floating wind turbine performance is established using neural network techniques. • The mooring system parameters are optimized using genetic algorithms to minimize costs. [ABSTRACT FROM AUTHOR]