A Hybrid Multi-Objective Evolutionary Algorithm for Wind-Turbine Blade Optimization
Date
2013-04-30
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Abstract
A concurrent-hybrid non-dominated sorting genetic algorithm II (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 National Renewable Energy Laboratory's (NREL) 5MW wind-turbine blade. To estimate the aerodynamic and structural performance, blade element momentum (BEM) and beam models were developed and validated. 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 at lower computational cost than for a conventional MOEA. To compare the rate of convergence between the hybrid and non-hybrid NSGA-II on the NREL 5MW blade optimization, a computationally intensive case requiring 110,000 objective-function evaluations was performed using the non-hybrid NSGA-II. From this particular case, a 1.8% increase in the annual energy production and 4.7% decrease in the flapwise root-bending moment with the same mass as the NREL 5MW blade was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem. A study on the gradient quality shows that the numerical instability of BEM and beam models hinders suitable gradient calculations.
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Engineering--Mechanical
Citation
Sessarego, M. (2013). A Hybrid Multi-Objective Evolutionary Algorithm for Wind-Turbine Blade Optimization (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24761