Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms
作者： Mojtaba Tahani , Narek Babayan , Fatemeh Razi Astaraei , Ali Moghadam
DOI: 10.1016/J.ENCONMAN.2015.06.086
关键词: Chord (geometry) 、 Pareto principle 、 Genetic algorithm 、 Particle swarm optimization 、 Metaheuristic 、 Cuckoo search 、 Blade element momentum theory 、 Multi-objective optimization 、 Algorithm 、 Mathematics
摘要: The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum will be achieved by optimized In research study, multi objective optimization HATCT is carried out using four different algorithms and evaluated in combination with blade element momentum theory (BEM). second version non-dominated sorting genetic algorithm (NSGA-II), particle swarm (MOPSO), cuckoo search (MOCS) flower pollination (MOFPA) are selected algorithms. power coefficient produced torque stationary as functions chord twist distributions along span decision variables. These combined (BEM) for purpose achieving best Pareto front. obtained fronts compared each other. Different sets experiments considering numbers iterations, population size tip speed ratios. which MOFPA NSGA-II have better quality comparison MOCS MOPSO, but other hand a detail between first indicated that can obtain front maximize up 4.3% 57.9%. geometries last members produce maximum hyperbolic constant distributions, respectively.
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