Research And Implementation Of Maximum Power Point Tracking Algorithm For Photovoltaic Power

With the development of the economy,the problems of energy crisis and environmental pollution are becoming more and more serious.Vigorously exploring new types of clean energy and strengthening the practical application of renewable energy have become the main strategic tasks for all mankind to solve energy shortages and protect the ecological environment.Photovoltaic power generation is about to become one of the most popular power generation methods in new clean energy.At present,due to imperfections in photovoltaic cell materials and control algorithms,photovoltaic power generation efficiency is largely limited by the environment,and solar energy utilization rate is low.In a short time,the material of the photovoltaic cell is difficult to be greatly improved in a short time.Then,the improvement of the maximum power point tracking algorithm of the photovoltaic power generation system becomes an effective way to improve the power generation efficiency.The paper first introduces the background and significance of photovoltaic power generation at home and abroad.Secondly,the power generation principle and output characteristics of photovoltaic cells are expounded,and the physical model and mathematical model of photovoltaic cells are established respectively.Based on the mathematical model of engineering application,the simulation is carried out in Matlab/Simulink platform.Based on the simulation results,the influence of temperature and illumination conditions on the output characteristics of photovoltaic modules is analyzed.Then the Matlab/Simulink simulation model of the PV array is established to simulate the single-peak and multi-peak characteristics of the PV array output under normal lighting conditions and local shadows.According to the simulation results,the relationship between the maximum power point and the corresponding voltage is summarized,which provides a theoretical basis for the next study.The paper discusses the principle,design flow and parameter analysis of particle swarm optimization algorithm.For the shortcomings of low convergence precision and easy to fall into local optimum,an improved particle swarm optimization algorithm based on adaptive learning with escape device is proposed.The maximum power point tracking model based on adaptive particle swarm optimization(PSO)is constructed in Matlab/Simulink platform.The analysis can be used to solve the global optimization problem of the maximum power point under partial shading conditions.The simulation model of each module in the photovoltaic maximum power tracking control system is established.The whole photovoltaic maximum power tracking system is simulated based on Matlab/Simulink platform.The simulation results show that the proposed method can greatly improve the convergence accuracy of the algorithm,speed up the optimization of the algorithm,and enhance the versatility of the optimization parameters.In addition,by adding the early-maturing escape device,the problem that the PSO algorithm is easy to fall into the local optimal region is solved.The system's ability to optimize in the case of uneven shading is greatly improved,and the global maximum power point tracking is realized.