Optimization of the mooring system of a floating wind turbine by developing a surrogate model-assisted evolutionary framework

In the design and optimization of Floating Wind Turbines (FWTs), there are challenges related to the large number of design parameters and the need for efficient and accurate calculation of turbine dynamic responses. To address these issues, this study proposes a surrogate model-assisted evolutionary framework for the mooring system optimization of shallow-water wind turbines. This distinct feature of the optimization framework lies in that it employs a sparse polynomial chaos expansion surrogate model to quickly predict the performance indicator values of FWTs with different mooring configurations and adopts the differential evolution algorithm to find the mooring parameter combination with the best performance, achieving efficient, accurate, and automated multi-parameter optimization. The framework is utilized to optimize the mooring system for a FWT at a relatively shallow water depth, through defining a mooring performance evaluation indicator as the objective function that comprehensively considers mooring line tensions, platform motions, anchor tensions, and the mooring line material cost. Based on the optimization application, the accuracy of the optimization framework is verified. The dynamic responses and safety assessments of the optimized FWT in the fatigue limit state (FLS) and ultimate limit state (ULS) are conducted. The results demonstrate the effectiveness of the proposed optimization framework in enhancing a FWT's performance according to flexibly defined objective functions.