Abstract
Most global wind resources are found in water depths exceeding 60 meters, where using bottom-mounted structures is challenging and costly. Thus, increased the studies on floating offshore wind turbine (FOWT) solutions characterized by complex dynamics between the floating structures and turbines. Nowadays, one key challenge is designing optimum mooring arrangements for large-scale FOWT.
A meta-heuristic solution is recommended to solve a complex optimization problem, such as the mooring arrangement of a floating wind turbine. Among these, the Evolutionary Algorithms (EA) use mechanisms inspired by biological evolution. Genetic Algorithm (GA) is a multi-objective search and optimization algorithm based on the concept of Darwin’s theory of evolution, and it belongs to the class of Evolutionary Algorithms. It combines the idea of survival of the fittest with randomized information exchange.
Based on the literature, multi-objective optimization algorithms, such as the GA, are commonly used with hydrostatic and frequency-domain models, which are less accurate than a time-domain model for optimization purposes of FOWT.
This work addresses the optimization of the mooring system of a FOWT using time-domain simulations in OpenFAST. An in-house code is developed to handle calculations on OpenFAST and apply GA to find the optimal mooring arrangements for specific design variables meeting predefined multi-objectives, respecting all specific constraints for the FOWT. Finally, the optimized arrangements are checked against Ultimate Limit State (ULS) and Accidental Limit State (ALS), performed for the mooring system in damaged condition.
The developed optimization process is applied to the UMaine VolturnUS-S semisubmersible platform to maximize power quality and minimize the costs of the lines.
