Extreme Search Control Problem And Its Application In Photovoltaic Power Generation

With the intensification of the fossil energy crisis and environmental pollution issues,solar energy has attracted much attention as a clean and sustainable new energy source.In particular,the photovoltaic power generation industry has developed rapidly in recent years.Under the condition that photovoltaic power generation technology is becoming mature,there is still a problem that power generation efficiency needs to be improved.In particular,the issue of Maximum Power Point Tracking(MPPT)in complex situations such as shaded shadows is still a hot and difficult issue at the moment.This paper starts from the basic module of the realization of MPPT control in photovoltaic power plants,and gradually deepens the theoretical research in the application of practical projects,selects a global particle search algorithm with good extremum search performance to re-complete the application in photovoltaic MPPT control,and for its application The shortcomings are improved.The specific research content of this paper is as follows:(1)Firstly,we provide different types of photovoltaic power generation systems and basic modules such as PV arrays and DC/DC controllers that implement photovoltaic MPPT control,and combine the existing research to give a concrete implementation of the mathematical model.Then in the third chapter,it focuses on reiterating photovoltaic MPPT control,pointing out that the implementation of photovoltaic MPPT is a control process.The extreme value search algorithm is generally designed as a power feedback link in photovoltaic MPPT control.The relevant theoretical research cannot be compared with ordinary optimization.The problem is the same.Finally,on the basis of modeling and control design,a numerical simulation experiment platform with local shading effect is given.(2)Select standard PSO algorithm for PV MPPT control research.Photovoltaic power generation system does not have parallel characteristics during operation.Therefore,each particle in the particle population generated by the PSO algorithm must be input into the photovoltaic power station system in turn,and the relatively accurate output power can be fed back after the system is stabilized.To solve the above problems,the particles of each generation should be dispersed in different control cycles in time to complete the photovoltaic MPPT control process and power feedback.When the current generation of particles completes the output,the new generation of particle groups is planned and the cycle is continued until reaching certain conditions exit.A comparative numerical simulation experiment with different parameters was designed to study the influence of related parameters on the photovoltaic MPPT control achieved.(3)Improve the PSO algorithm to achieve photovoltaic MPPT control.The results of the numerical simulation experiment of photovoltaic MPPT control implemented in the standard PSO algorithm lead to two major problems in the application: There is a large range of jump between adjacent particles output by the algorithm,which will bring great instability to the photovoltaic power generation system in practical application;After each particle is input into the photovoltaic power generation system,its adjustment time is difficult to determine,and power feedback errors can cause serious consequences,making it difficult to speed up the search process.Based on the characteristics of the algorithm and the above problems,the improved Logistic chaotic map is proposed to initialize the population,sort the population particles,and then output to the photovoltaic power generation system and to improve the global optimal and individual optimal particle location update strategy.The solution is applied to the photovoltaic MPPT control to solve the problem.The problem of non-convergence of the algorithm due to the large range jitter of adjacent outputs and inaccurate feedback power has significantly improved the performance of photovoltaic MPPT.