Research On MPPT Control Method Of Photovoltaic Power Generation System

Due to the increasing global warming and the depletion of fossil fuel energy,countries around the world are paying more and more attention to the field of clean energy and renewable energy power generation,the governments are calling for the use of renewable energy to cope with the shortage of traditional fossil energy.Photovoltaic power generation is the most promising renewable energy technology.The photovoltaic power generation system has the advantages of low maintenance cost,free operation,no complex components,environmental friendliness,and sustainable development.Light intensity and ambient temperature are still important factors for photovoltaic power generation,so it is crucial to achieve accurate maximum power point tracking under uneven illumination.The traditional maximum power point tracking algorithm may fall into a local optimum when faced with uneven lighting conditions,which greatly affects the tracking efficiency.Although the intelligent algorithm can track the maximum power point under local shadow conditions,it still has defects in tracking accuracy and tracking speed.Therefore,this dissertation studies the MPPT control method under the condition of partial shading.Firstly,combined with the actual situation and national policy situation,the research background and research significance of the topic selection are expounded.After studying the output characteristics of photovoltaic arrays under two conditions of uniform and nonuniform light intensity respectively,for partial shadow and bypass the influence of diodes on photovoltaic cells was designed,and an improved multi-component photovoltaic power generation system was designed,and a better mathematical model was established.Secondly,the shortcomings and deficiencies of traditional tracking algorithms and other original intelligent algorithms are analyzed.Based on this,the gray wolf algorithm is introduced into the photovoltaic MPPT tracking technology.Through the derivation and analysis of the formula,this dissertation proposes an improved gray wolf algorithm.The improved algorithm optimizes the location determination strategy of the head wolf,and combines the classical gray wolf method with the DLH hunting search strategy,which strengthens the balance between local and global searches and maintains diversity.This dissertation innovatively combines the improved gray wolf optimization algorithm with the disturbance observation method,which improves some shortcomings of the original algorithm and improves the system efficiency.Finally,the system simulation experiment of the proposed algorithm is carried out.Simulink in MATLAB environment is used for system modeling and simulation,and the various components of the simulation model are introduced.The simulation experiment proves the effectiveness and superiority of the composite algorithm proposed in this dissertation to track the maximum power point of photovoltaics under partial shading conditions.