Genetic Algorithm with Gradient Based Optimization for Wind Turbine Blade Design - Kim, You Jin; Kim, Minjun; Al-Abadi, Ali Kedher Kashin; Delgado, Antonio (11th EAWE PhD Seminar on Wind Energy in Europe, 2015)
The gradient-based optimization method for blade design and a stochastic optimization method for airfoil design are combined to generate improved aerodynamic performance of the Horizontal Axis Wind Turbine (HAWT). Torque Matched Aerodynamic Shape Optimization method (TMASO) is developed to optimize blade design using gradient based optimization and Genetic Algorithm (GA) is used to optimize airfoil as a stochastic optimization method. Blade shaped by TMASO is incorporated with GA optimized airfoils distributed inside the blade with a span-wise direction. In TMASO, the rotor torque of corresponding blade is calculated with considering chord and pitch angle distribution of the balde. The calculated rotor torque is compared to experimentally measured torque of generator for finding the solution of minimum differences of rotor torque and generator torque and maximum value of power coefficient. The control points of B-spline, used to parameterize airfoil, are set as variables in GA. While upper and lower bound of y-values of B-spline control points are limited, the GA runs to find the optimum y-points that generates the most effective airfoil based on the objective function of highest Gliding Ratio (GR) value of airfoil from XFOIL. The resulted power coefficient (C𝑃 ) of designed wind turbine is more stabled and higher at different TSR (Tip Speed Ratio) than the reference wind turbine, NREL UAE Phase-VI. GR values of the optimized airfoil and S809 airfoil of the reference wind turbine, are compared with boundary layer investigation of airfoils to extra validate the performance improvement of optimized wind turbine. <br
Keywords: Airfoil; Genetic Algorithm; Gradient-based; Optimization; Wind turbine; aerodynamics; Boundary layer