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

Abstract

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.

Keywords:

• hybrid multi-objective evolutionary algorithm
• multi-objective optimization
• blade element momentum theory
• beam theory

Notes

1. The evaluation of all objective functions in the objective vector is considered as one objective-function evaluation.

2. A maximization of the AEP is performed by minimizing the negative value of its objective function.

Additional information

Funding

The project was funded by the Natural Sciences and Engineering Research Council's Industrial Postgraduate Scholarship program; Siemens Energy Inc.