Abstract
Aiming at failure problems associated with floating offshore wind turbine (FOWT) mooring systems, a new reliability assessment method is established based on an intelligent algorithm. Using the NREL 5 MW OC4 semi-submersible wind turbine as the research object, the hydrodynamic is obtained, together with the aerodynamic, and axial tension forces of the mooring chains under different working gust conditions through integrated simulations. The intelligent teaching-learning-based optimization (TLBO) and improved teaching-learning-based optimization (ITLBO) algorithms are proposed to calculate the reliability of the FOWT mooring system. The results show that the reliability index of the windward chain is 4.21 under an extreme working gust, which is less than the code recommended value 4.26 and should be further evaluated. The penalty coefficient, teaching factor, and population number have a great impact on the convergence speed of the TLBO algorithm. Compared with traditional reliability algorithms, such as the first-order second-moment algorithm and Monte Carlo algorithm, the TLBO algorithm has higher efficiency, especially the ITLBO algorithm, which has the fastest convergence speed by constantly adjusting the teaching factor. Therefore, it is recommended to use the TLBO or ITLBO algorithm to evaluate the reliability of FOWT mooring systems.
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