Two-stage Particle Swarm Algorithm MPPT Control Based On Hybrid Leapfrogging

With the exhaustion of traditional energy sources and the increasingly serious environmental problems,people's attention is more focused on the development and utilization of new energy sources.Compared with other renewable energy sources,solar energy has the advantages of large energy reserves,good environmental benefits,and strong economy,so it has been widely used.With the continuous improvement of social productivity,photovoltaic power generation systems and related industries have become the fastest-growing industries,and research on photovoltaic power generation systems has become increasingly important.In the study of photovoltaic power generation systems,the most critical issue is the low photoelectric conversion efficiency of photovoltaic cells,which is susceptible to environmental changes.Therefore,it is necessary to control the algorithm to make the output power of the photovoltaic panel reach the optimal state.First,referring to a large number of relevant literature,comparatively studies the pros and cons of the maximum power point tracking algorithm of various photovoltaic power generation systems,and finds that the traditional control algorithm lacks the global search capability and is only applicable to the environment of uniform illumination.In real life,photovoltaic cells are often affected by external factors or human factors to cause local shading,which makes the P-U curve appear muti-peaked.Therefore,intelligent control algorithms are usually used to achieve maximum power point tracking control.And according to the working principle of photovoltaic cells,and build a PV model in Matlab/simulink,by changing the ambient temperature and light intensity to simulate the I-U and P-U curves of photovoltaic cells under uniform lighting conditions and partial shading conditions.The advantages and disadvantages of the traditional control algorithm are analyzed and compared,and it is concluded that in the muti-peak P-U curve,the traditional control algorithm does not have a global search capability,which causes failure,and is not suitable as a control algorithm under local shading conditions.Secondly,the working principle of the standard particle swarm optimization algorithm is analyzed.It is found that in the muti-peak maximum power point tracking,although the standard particle swarm optimization method has the global search capability,it needs a larger population to cover all the optimal within a certain range solution.In addition,the search speed cannot be too high,so as not to miss the best particles,so the standard particle swarm algorithm convergence speed is slow.On the other hand,if the search speed of the particles is too fast,the search accuracy may below,and the local optimization is re-entered.Finally,a two-stage particle swarm optimization algorithm based on mixed leapfrog is proposed to improve the convergence speed and search accuracy,and reduce the steady-state oscillation.The grouping method of the mixed leapfrog algorithm is added to the standard particle swarm optimization algorithm to ensure fast and accurate search for the global extremum.The improved particle swarm optimization algorithm also introduces an adaptive speed factor,which further improves the convergence speed of the algorithm.And through the simulation module in Matlab/simulink,the standard particle swarm optimization algorithm and the two-stage particle swarm optimization algorithm based on mixed leapfrog are simulated and analyzed respectively,which verifies that the two-stage particle swarm algorithm is superior to the standard particle swarm in tracking speed and steady-state oscillation algorithm.