Research On Maximum Power Point Tracking Strategy For Photovoltaic Power Generation

Fossil energy has played an irreplaceable role in the development of human society and economy,greatly promoting the development of economic society and the progress of human civilization.However,various environmental damages caused by the massive use of fossil energy have also emerged,so it is particularly important to find a clean and renewable energy source.With its superior characteristics,solar energy has quickly become a promising renewable energy source,especially in the context of the global energy revolution in recent years and China’s"carbon peak+carbon neutrality",solar power generation stands out among various new energy power generation.However,solar power generation has shortcomings such as being easily affected by the external environment,resulting in low power generation efficiency,and prone to multi-peak phenomena under uneven illumination,which limits the further development of solar power generation.Maximum power point tracking(MPPT)technology is an important way to improve power generation efficiency and photoelectric conversion speed in photovoltaic power generation,so studying MPPT technology is of great significance in photovoltaic power generation.This paper conducts the following research on maximum power point tracking for photovoltaic power generation:Firstly,the photovoltaic array is modeled,and the output characteristics of the photovoltaic array under uniform illumination and local shadows are analyzed.Corresponding solutions are proposed for the factors that affect the output characteristics of the photovoltaic array.The perturbation observation method,conductivity increment method,and constant voltage method are introduced into the photovoltaic array to optimize the maximum power point,and simulation is conducted to analyze the photovoltaic output characteristic P-t curves,P-V,I-V characteristic curves,and P-V,I-V characteristic curves of the three conventional methods under both uniform and non-uniform illumination.Conventional methods have poor convergence characteristics,low accuracy of maximum power point tracking,and slow convergence speed Easy to fall into local advantages and other shortcomings.Secondly,in view of the shortcomings of conventional methods,a maximum power point tracking strategy based on the Whale algorithm is proposed,and the shortcomings of the standard Whale algorithm are improved by introducing a nonlinear convergence factor to replace the linear convergence factor,improving the diversity and search speed of the population;By adding a Tent chaotic map,the population variables are transformed into an interval range to reduce errors,making it difficult for individuals to fall into local convergence and not having an impact on the randomness of population initialization.Conduct optimization testing and convergence analysis on multiple internationally common test functions for the improved whale algorithm to verify the effectiveness and convergence of the improved whale algorithm.At the same time,the particle swarm optimization algorithm and the standard whale algorithm also use multiple internationally commonly used test functions for optimization testing and convergence analysis.The convergence results are compared with the convergence results of the improved whale algorithm,which proves that the improved whale algorithm has the best convergence.Finally,the improved whale algorithm is applied to the maximum power point optimization,and the model is simulated in Matlab/Simulink.The illumination conditions are set to keep changing.At the same time,the simulation results of MPPT using particle swarm optimization and standard whale algorithm under the same conditions are compared.Finally,this article also applies the dung beetle algorithm to maximum power point tracking,and improves the dung beetle algorithm in the foraging stage.By introducing the slime fungus algorithm into the standard dung beetle algorithm,an improved dung beetle algorithm is proposed.In order to verify the feasibility and convergence of the improved dung beetle algorithm in finding the optimal solution,it was applied to five different test functions,while setting the same conditions to test the convergence and feasibility of the standard particle swarm optimization algorithm,the improved whale algorithm,and the standard dung beetle algorithm.The improved dung beetle algorithm is applied to the maximum power point tracking,and the model is simulated and verified in Matlab/Simulink.The external lighting conditions are set to three stages,and the lighting conditions in each stage are 1000W/m~2,800W/m~2,and 400W/m~2.The same lighting conditions are maintained,while the MPPT using the standard particle swarm optimization algorithm,the improved whale algorithm,and the standard dung beetle algorithm is simulated and verified.After simulation and comparing the simulation results of the four algorithms,it was found that the MPPT using the improved dung beetle algorithm can quickly find the maximum power point under changing lighting conditions,with minimal energy loss.