Wind blade chord and twist angle optimization by using genetic algorithms

This paper shows a method to compute the chord and twist distributions in wind power blades. The distributions are computed to maximize the mean expected power depending on the Weibull wind distribution at a specific site. This approach avoids assumptions about optimal attack angle related to the ratio between the lift to drag coefficients. To optimize chord and twist distributions, an efficient implementation of the Blade-Element and Momentum theory is used. In the implementation, the sophistication is dismiss to reduce computational cost. The time required to evaluate the forces in a typical turbine is in the order of milliseconds, which allows massive evaluation of trial turbines. The implementation is validated by comparing power prediction with the experimental data of the Risø test turbine. High quality in results is obtained until the stall zone, about wind speed of 13m/s proximately. Predictions are used to compute the mean power that is used as the fitness function in a genetic algorithm. An application is presented to optimize the blade of this test turbine for a specified wind distribution.