Research On The Lion Group Algorithm For Maximum Power Point Tracking Of Photovoltaic Generation

Nowadays,the energy crisis has gradually attracted the attention of all countries in the world.Solar energy has become one of the most important new energy sources.However,in the application process of solar energy,the photoelectric conversion efficiency of photovoltaic array is low,and the output characteristic is unstable in an uneven environment.The P-U output characteristic curve of photovoltaic cell will have multiple extreme values,and the maximum power point tracking can’t be accurately located.Therefore,the algorithm research on this process is very critical.This paper mainly carries out the following research:(1)Establishment of photovoltaic array model and analysis of output characteristics.The mathematical model of photovoltaic cell is constructed,and the application model in practical engineering is deduced.A series-parallel photovoltaic array mathematical model is built by parallel bypass diode.The control variable method was used to analyze the output characteristics of photovoltaic cells and photovoltaic arrays under full light and local shadow conditions,and the output characteristic curves under abrupt environmental changes were analyzed.In order to track the maximum power point,the DC-DC converter should be set before the load so that the maximum power can be tracked smoothly by adjusting the duty cycle.(2)Analysis of maximum power point tracking algorithm for photovoltaic power generation.The principle of maximum power point algorithm is analyzed,and some classical tracking algorithms,such as constant voltage method,disturbance observation method and conductance increment method,are listed.Through the analysis and research of the tracking process of the classical algorithm,the tracking effect is simulated,and the advantages and disadvantages of the three algorithms are compared.(3)Research on an improved new lion swarm algorithm.In view of the situation that the traditional algorithm is easy to fall into the local extreme value,the lion swarm algorithm is analyzed.Based on the original algorithm,chaos idea is introduced,the chaotic variables are transformed to the interval range,and the position of the lion swarm is initialized through the Logistic chaotic sequence.Then,the Levy flight mechanism is combined with the lion swarm algorithm.Fitness value is the worst of the introduction of the previous iteration as reference quantity of step improvement,the lion king in short distance step combined with occasional long jump to small probability area,a random disturbance to the position of the lion king,he was able to quickly distribution in the whole optimization space,expand the search scope of lions,jump out of local optimal solution,further improve the accuracy of the algorithm,And the convergence speed of the algorithm is improved.(4)Proof of convergence and parameter selection of the new lion swarm algorithm.In this paper,the basic flow of the improved new lion swarm algorithm is described and the convergence of the algorithm is proved.Six classical multi-peak test functions are selected for the global optimization of the improved new lion swarm algorithm,and the simulation analysis is compared with the original lion swarm algorithm.The four parameters of the improved algorithm are simulated continuously,and the effects of the total number of lions,the proportion,the distribution radius and the maximum number of iterations on the tracking performance of the algorithm are analyzed in detail.(5)Simulation verification of the improved new lion swarm algorithm in MPPT.The simulation model of photovoltaic array is built: Boost circuit,PWM controller and S-function control module,and the simulation verification of the improved new lion swarm algorithm is carried out under the condition of double peak and three peak.The output results show that the efficiency of the improved algorithm is significantly improved,the number of iterations is reduced,the convergence time of the algorithm is shortened,and the maximum power point can be accurately tracked when local shadows appear in the photovoltaic power generation system.