Research On Multi-peak MPPT Control For PV System Under Partial Shadow Based On Improved Bat Algorithm

With the continuous development of science,the number of smart products is increasing.Electricity has become an indispensable energy source for our daily lives.Photovoltaic power generation has attracted widespread attention due to its renewable and pollution-free advantages.However,there are some technical problems with photovoltaic power generation,the problem of how to maximize the power of photovoltaic cells in the work process is particularly prominent.When photovoltaic cells are partially shaded,P-V curve was multimodal.Due to the shortcomings of being easily trapped in local optimum,traditional algorithms are no longer suitable for multimodal cases.Although some intelligent algorithms will not fall into the problem of local optimization and can search the actual maximum power,because of the long search time,there is no obvious advantage.So,this article uses improved bat algorithm and how to apply this algorithm to the problem of Maximum Power Point Tracking(MPPT).The main tasks completed are as follows:Building a partial shade model: In actual situation,photovoltaic cells are usually partially shaded.Therefore,we need to build a partial shade model.Models can be built using MATLAB / Simulink.Introducing improved bat algorithm: The problems and shortcomings of these two types of algorithms are obtained through the analysis of traditional algorithms and other intelligent algorithms(Particle Swarm Optimization,PSO),which leads to the improved bat algorithm used in this study.This algorithm optimizes the convergence factor of the original algorithm and applies of Improved Bat Algorithm,Traditional Algorithm and Other Intelligent Algorithms to Photovoltaic MPPT in Local Shadow.Building a maximum power point tracking model: Without photovoltaic cells being partially shaded,like the traditional algorithm can find the maximum power point.However,the maximum power point cannot be found in the case of partial shading.Although other intelligent algorithms can find the maximum photovoltaic power point under the condition of partial shading,it costs long time.To address these two types of issues,we build a maximum power model in MATLAB / Simulink and analysis of the plotted power-time curve.We can verify the previous conclusion that the traditional algorithm cannot get the maximum power point.Although other intelligent algorithms can search the maximum power point,it takes a long time and it is concluded that the improved bat algorithm can not only find the maximum power point but also for other intelligent algorithms,it takes less time and less time than the original algorithm.Thus verifying the speed improvement of the improved bat algorithm.Physical platform verification: To verify the effectiveness of the improved bat algorithm in practice,we test on V-SUN-S4000 photovoltaic power generation training platform and plot power-time curve.Thus verifying that the algorithm can track the global maximum power in the case of local shading.To sum up,whether it is simulation results or physical platform verification,Improved Bat Algorithm can show better tracking effect.we verify the improved bat algorithm's tracking effect is good and the time required for searching is short and significant in the study of maximum power point tracking in the case of partial shading.