Genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: A continuous approach or a binary one?

Abstract

The continuous and the binary techniques are the main types of genetic algorithms that could be employed for an optimization problem. The study investigates the robustness and accuracy of each technique for the wind turbine blades design and optimization problem. For that purpose, the geometry of the blade was designed for the maximization of the output power, the desirable goal of the blade design. The design variables consist of the distribution of the chord and twist along the blade. Results indicate that the continuous genetic algorithm outperforms the binary one from the standpoint of the accuracy and also the computational time. However, the use of uniform crossover could improve the convergence rate of the binary genetic algorithm. Moreover, the sensitivity analysis of the applied genetic algorithms with respect to the population size and the mutation rate was performed to find the appropriate values for those parameters. The outcomes emphasized that adopting a small number of population size together with a large number of generations could speed up the convergence rate of the problem. Also approved in the study was the efficiency of the so-called “Superblade” operator which improved the convergence rate of the algorithm and resulted in the powerful blades.