Modeling and repositioning control of floating offshore wind farms via mooring length reconfiguration for power enhancement and fatigue load mitigation

Floating Offshore Wind Turbines (FOWTs) have emerged as a key technology for harnessing deep-sea wind resources due to their adaptability to complex marine environments and the growing energy demands. However, wake interactions among turbines often reduce power generation and increase fatigue loads, highlighting the necessity for effective farm-level repositioning strategies. This paper proposes an optimization framework for farm-level repositioning control of Floating Offshore Wind Farms (FOWFs), in which individual mooring line lengths are treated as operational control inputs to dynamically adjust turbine positions and enhance wind farm performance. An integrated steady-state simulator is developed to enable control-oriented optimization of wind farm performance, allowing continuous assessment with significantly reduced computational demand. To efficiently solve the optimization problem, a History-Informed Simulated Annealing Optimization (HISAO) algorithm is proposed, which leverages elite solution knowledge and history-feature-guided strategy to enhance robustness and convergence speed. An interactive visualization platform is further introduced to close the loop from simulation to optimization and result assessment. It renders FOWF layouts, wake fields, and mooring configurations with side-by-side pre/post-control comparisons, facilitating expert review and decision communication. A parametric feasibility analysis establishes a viable mooring line adjustment range of up to 30 m under extreme conditions, ensuring engineering applicability. Multi-scenario simulations further validate the efficiency and effectiveness of the method: the simulator achieves a 99.85% reduction in computational cost compared to OpenFAST, while improving the power output of a 1×3 FOWF by 6.17% and reducing turbulence intensity by 0.22%.