Application of Genetic Algorithm for Mooring Optimization of Floating Offshore Wind Turbine

The studies and the production of wind energy itself has been rising in many parts of the world due to the increased need for energy generation and the pressure from all around the world to look for renewable energy. Although the generation of energy from winds is already well known, the largest source of winds and with the least environmental effects are in waters further from the coast, in regions with higher water depth. When exceeding 60m in depth, the use of fixed wind turbines becomes challenging and costly. Therefore, there is a growing need to study the feasibility of using Floating Offshore Wind Turbines (FOWT), with appropriate choice of the respective mooring arrangement.

An optimization algorithm that has been widely used in the study of engineering problems is the Genetic Algorithm (GA). Belonging to the class of Evolutionary Algorithms (EA), the Genetic Algorithm is a multi-objective search and optimization algorithm based on the concept of Darwin's theory of evolution.

According to the literature, multi-objective optimization algorithms usually work with models in the frequency domain. The present work presents a proposal for using GA to optimize the mooring system of a FOWT using time domain software. Through an in-house code developed to apply GA and handle calculations using time-domain analysis in OpenFast, the optimization of the mooring arrangement of a FOWT with specific parameters can be found for different environmental conditions and respecting all defined constraints. In addition to optimization, the methodology applied seeks for systems that can be applied respecting the certification requirements for Ultimate Limit State (ULS) and Accidental Limit State (ALS), performed for the mooring system in damaged condition.

This methodology is applied to optimize the mooring system for the UMaine VolturnUS-S semisubmersible platform designed to support the IEA 15-MW reference wind turbine with the objective of maximizing the quality of the power generated and minimizing line costs.