Power coefficient in wind power using particle swarm optimization

An optimization methodology is introduced to maximum the power coefficient by optimizing the wind velocity and angle of attack of horizontal axis wind turbine. The S809 airfoil with 10% thickness is selected. The blade element momentum theory is the method used for optimization. The feed forward neural network is first used to train the coefficient of lift and coefficient of drag by giving the Reynolds number and angle of attack as input parameters and the technique used is back propagation error. The Particle Swarm Optimization code is created in MATLAB for S809 airfoil. The input parameters to be optimized are wind velocity and angle of attack. The trained parameters obtained from the feed forward neural network are also feed into the code. The population is selected and the blade element momentum theory is used to find the power efficient which should be near the fitness function and finally new generation is obtained with the optimized angle of attack and wind velocity.