Research On Maximum Power Point Tracking Of Photovoltaic Array Based On Improved Grey Wolf Algorithm

Due to the large consumption of traditional energy sources,the search for new and renewable energy sources has become a major issue that the society urgently needs to solve.With the rise of new energy sources,countries have begun to invest in the photovoltaic industry and formulate corresponding policies to stimulate the development of the photovoltaic industry.The core problem of photovoltaic power generation is to find the maximum power to get the most benefit.When the PV array is under local shadow conditions,the P-U curve exhibits multi-peak shape,and the traditional algorithm will fall into local optimum and fail,and the true maximum power cannot be found.While existing intelligent algorithms can be tracked,they are not dominant in search time.Therefore,this paper adopts the improved gray wolf algorithm and studies how it can be applied to the maximum power point tracking of photovoltaic power generation systems.The main tasks completed are as follows:(1)Analysis and comparison of existing tracking algorithmsThe traditional tracking algorithm and other intelligent algorithms are compared and analyzed,and the grey wolf algorithm is introduced for each problem.Through formula derivation and computational analysis,the improved gray wolf algorithm is adopted in this study.The algorithm optimizes the convergence factor of the original algorithm,adds dynamic weights,and applies the improved gray wolf algorithm to the tracking of the maximum power point of the PV array under local shadow.(2)Establish a local shading simulation model and perform simulation testIn view of the problem that the traditional algorithm cannot track the global maximum power when the shadow blocks the photovoltaic array,the maximum power tracking model is built in MATLAB/Simulink,and the photovoltaic block is blocked in series,and the two solar cells are blocked in series.In two cases,compared with the improved gray wolf algorithm,the power-time curve is drawn,and the experimental results are analyzed.The conclusion is drawn that the traditional tracking method cannot track the maximum power of the shadow;the original gray wolf algorithm and the improved gray wolf algorithm search time The problem is to set up parallel blocking of two photovoltaic cells,and block the two shades of the three photovoltaic cells,draw the power-time curve,compare the original gray wolf algorithm with the improved gray wolf algorithm search time,and verify the improved gray wolf algorithm in speed.(3)Physical platform verification testIn order to verify the effect of improving the grey wolf algorithm in the actual photovoltaic power generation system,the V-SUN-S4000 photovoltaic power generation training platform was used for testing.The platform includes photovoltaic power supply system,inverter and load system and real-time data monitoring system.Photovoltaic cells operate at a voltage rating of 17.6 V and a current rating of 1.14 A.The two shade states of the occlusion of a photovoltaic module and the occlusion of two photovoltaic modules are respectively set.After many experiments,the photovoltaic output power curve is measured in the real-time monitoring system,and the algorithm can track the global maximum power in the local shadow.In short,whether it is the simulation results or the test platform verification,the improved gray wolf algorithm can show good tracking results under local shadow.It shows that the improved gray wolf algorithm has good tracking effect and fast searching time,which effectively improves the power generation efficiency of the photovoltaic array,and has important application value for the future photovoltaic power generation system.