Genetic Algorithm Optimization Design for Wind Turbine Blade Aerodynamic Shape

This text presents an optimization design approach for wind turbine blade aerodynamic shapes using genetic algorithms, enabling faster and more efficient design processes. Genetic algorithms are computational methods that simulate natural selection and evolutionary processes, searching for optimal solutions through operations mimicking genetic crossover and mutation. By implementing genetic algorithms with appropriate fitness functions that evaluate aerodynamic performance metrics (such as lift-to-drag ratios and power coefficients), we can enhance wind turbine blade performance and efficiency, thereby maximizing wind energy utilization. The optimization process typically involves encoding blade parameters (including chord length distributions, twist angles, and airfoil shapes) into chromosomes, then iteratively improving designs through selection, crossover, and mutation operations. This optimization methodology contributes to better outcomes and benefits in the wind power generation sector by systematically exploring design spaces that traditional methods might overlook.