The Research On Multimodal Intelligent Clusters Control Of PV System MPPT For Fog Area

The energy crisis and environmental pllution problems have become increasingly prominent,which makes human develop and use new energy imminently.Under this background,solar energy received the attention of the various countries for the advantaged superiority.A large number of photovoltaic power generation systems have been built to satisfy the requirement of human society.Therefore,independent distributed photovoltaic power generation has widely applied in power supply for remote areas and off-grid electrical elements,in this thesis,this kind of power supply can combine with the motorway foggy area’s electricity demand perfectly.As the maximum output power of the photovoltaic(PV)cell is constantly changing with external conditions changing,and remote areas are typical representative of the multi-modal situation,so if we want to acquire the optimal power of PV cell,maximum power point tracking(MPPT)is the most effective means.The main studies of this paper are as follows:First of all,this paper does an analysis of the development status and trends of photovoltaic industry at home and abroad,the research status of photovoltaic MPPT technology,and the purpose and significance of this investigation.Then,this paper analyses the classifications of photovoltaic power generation system,the PV cell’s principle model and the engineering mathematical model.Combine with the engineering model of photovoltaic cell,the MATLAB simulation model for PV cell is built,and the characteristics of PV cell are analyzed based on the simulation model,and several classical MPPT methods are described and analyzed.Again,The related theoretical knowledge of the multi modal intelligent cluster control(MICC)method is illustrated in brief,including the neural network technology,particle swarm optimization(PSO)algorithm and the improved PSO algorithm.At the same time,the framework and implementation process of the multi modal intelligent cluster control(MICC)method are mainly discussed.Moreover,the circuit structure and working principle of the DC-DC transform circuit which can realize the maximum power tracking is analyzed.Finally,under the MATLAB/SIMULINK experimental environment,the simulation of different MPPT methods have carried out and the experimental results have analyzed and compared.The specific contents include: combine with neuralnetwork technology and improved particle swarm optimization algorithm,established the PV intelligent reconstruction model based on multi neural network,the accuracy and error of four different reconstruction model are compared and analyzed;The MPPT modules for perturbation and observation method(P & O),the incremental conductance(INC)method,fuzzy control(FUZZY)method are established respectively,and implemented the maximum power point tracking of the independent photovoltaic power generation system under illumination rising and falling,temperature rising and falling;The realizing process of MICC method has elaborated,the classification of the mode,the structure of the multi neural network and the fuzzy controller are analyzed and explained in detail,and the method is applied to the maximum power tracking control under different environmental conditions,and good tracking performance is obtained;Under the condition of standard and non-standard,completed the experiment of the four MPPT methods,and the performance of the four methods are compared in the form of graphs and tables.The simulation results show the MICC method which put forward in this research has the best tracking performance,the power loss is the smallest and the power loss rate is the lowest.