10 MW FOWT Semi-Submersible Multi-Objective Optimization: A Comparative Study of PSO, SA, and ACO

• Published in: Energies (Basel) 2024-12, Vol.17 (23), p.5914
• Main Authors: Drabo, Souleymane, Lai, Siqi, Liu, Hongwei, Feng, Xiangheng
• Format: Article
• Language: English
• Subjects: Air-turbines; Algorithms; Alternative energy sources; Annealing; ant colony analysis; Case studies; Comparative analysis; Construction costs; Cost analysis; Design; evolutionary algorithms; Floating Offshore Wind Turbine; Genetic algorithms; Machine learning; Mathematical optimization; metaheuristic; Methods; Neural networks; Offshore; Optimization algorithms; Optimization techniques; Particle Swarm Optimization; renewable energy; Systems stability; Turbines; Wind farms
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17235914

The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm’s convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model.