An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

作     者：Wang, Haixin Shen, Jianxin 

作者机构：Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：2823-2827页

核心收录：

基　　金：Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX18_0317] 

主　　题：Simulation photovoltaic maximum power tracking artificial neural networks mind evolutionary algorithm 

摘      要：Aiming at the large error of the traditional constant control method in predicting the maximum power of solar UAV, this paper proposed an improved mind evolutionary algorithm combined with BP neural network (BPNN), in which the improved mind evolutionary algorithm optimizes the BPNN. The optimized model is used to predict the voltage at the maximum power of the panel in the UAV. The constant voltage parameter based on the conventional constant pressure control algorithm is replaced by this value. At the same time, a new control simulation model of constant voltage solar panel maximum power tracking based on the improved mind evolution algorithm optimize BPNN was built. At last, the algorithm was simulated and validated in the MATLAB/Simulink environment. The simulation results show that this algorithm is better than simply using BPNN and genetic algorithm optimize BPNN stability and higher accuracy.