Study Of Chaotic Improved Swarm Intelligent Algorithm And Its Application In The Photovoltaic MPPT

Swarm intelligence algorithm is used widely,because it has obvious advantages in solving the optimization problem with multiple local extreme points.However,because of the shortcomings of the algorithm itself,the application of swarm intelligence algorithm is limited.In this thesis,according to the disadvantages of swarm intelligence algorithm,a chaotic improved swarm intelligence algorithm is proposed.With ABC(artificial bee colony)algorithm and CSO(cat warm optimization)algorithm as examples,the detailed improvement is provided according to the original mathematical model of the algorithm.The simulation takes the photovoltaic as the background,builds the photovoltaic system model under the actual complex situation,applies the improved algorithm in the photovoltaic MPPT(maximum power point tracking),and performs static characteristic analysis and dynamic characteristic analysis.The simulation results show that the proposed algorithm can solve the “premature” problem of the original algorithm,has higher accuracy and faster convergence speed,and the robustness of the algorithm is good.Finally,the superiority of the proposed algorithm is verified by comparing the improved ABC algorithm and CSO algorithm with the PSO(particle swarm optimization)algorithm.In order to further verify the effectiveness and efficiency of the algorithm,a low-power experimental platform of photovoltaic system is built.The program of prior and improved algorithms was written.The curves of the voltage,current and maximum power value of the photovoltaic array are obtained.The experimental results show that the original algorithm and improved algorithm can quickly track to the new power point according to the change of light intensity.However,the tracking accuracy of the proposed algorithm is higher,and the average power value is larger.