Evolutionary Algorithms for Optimizing Wind Turbine Blade Design

Wind turbine blade design is a complex multi-objective optimization challenge, requiring a balance between aerodynamic efficiency, structural integrity, and cost minimization. This study proposes a hybrid optimization framework combining Genetic Algorithms (GA) for global exploration and Differential Evolution (DE) for local refinement. The hybrid GA-DE approach accelerates convergence and enhances solution quality by efficiently navigating the large design space. Experimental results show up to a 20% reduction in structural loads while maintaining high energy output, with a 15% faster convergence rate compared to traditional single-algorithm methods. The optimized blade designs achieved improved aerodynamic performance and reduced material costs, offering a more practical solution for next-generation wind energy systems. This research demonstrates the potential of hybrid evolutionary algorithms to drive innovation in renewable energy by delivering cost-effective and high-performance turbine blades, particularly for larger turbines requiring enhanced durability and power output.