A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algor...

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Veröffentlicht in:Engineering optimization Jg. 47; H. 8; S. 1043 - 1062
Hauptverfasser: Sessarego, M., Dixon, K.R., Rival, D.E., Wood, D.H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 03.08.2015
Taylor & Francis Ltd
Schlagworte:
beam theory
Bending
blade element momentum theory
Blades
Computational efficiency
Convergence
Evolutionary algorithms
Genetic algorithms
hybrid multi-objective evolutionary algorithm
Hybridization
multi-objective optimization
Optimization
Searching
Three dimensional
Tradeoffs
Turbines
ISSN:0305-215X, 1029-0273
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Zusammenfassung:A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2014.941532