Improved Snake Optimizer Algorithm And Its Application In Photovoltaic MPPT

People rely on the earth’s energy,which has led to serious environmental problems and energy crisis.Now in the context of the global energy revolution,it is imperative to actively seek clean energy.Electric energy,as the most common secondary energy used by humans,is closely related to social production and life.In the field of electric energy,abundant,renewable,and pollution-free solar power generation has great potential.How to improve the utilization rate of solar energy has always been the focus of research by researchers.Based on the current environment,this study focuses on photovoltaic power generation systems and delves into the key technology of Maximum Power Point Tracking(MPPT).This technology is a common method to improve photoelectric conversion efficiency,which can significantly reduce power generation losses and greatly improve the use of solar energy.Firstly,a mathematical model is established based on the working principle of photovoltaic cells,and a battery module model is built in MATLAB/Simulink software to simulate and analyze the output power characteristic curves of photovoltaic cells under various lighting conditions and temperatures.Photovoltaic cells often operate under local shading conditions,resulting in hot spot effects.A simulation model is built for local shading to analyze its output characteristics,and the working principle of the DC-DC converter in the boost circuit is explained in detail.By comparing two widely used traditional control MPPT methods and two population intelligence optimization control MPPT methods,and analyzing the principles and shortcomings of these four algorithms.Then,based on the problems of premature convergence,oscillation,and low accuracy in current traditional MPPT control algorithms,as well as the poor dynamic response performance and tracking efficiency of intelligent optimization algorithms,this paper introduces the Snake Optimizer algorithm to improve its tendency to fall into local optima.An Improved Snake Optimizer algorithm(ISO)is proposed.The ISO algorithm utilizes chaotic mapping to adjust the initialization position of the snake,improving the convergence speed of the algorithm’s optimization.In addition,the fixed development probability is improved to a dynamic development probability to improve the accuracy of algorithm optimization,and a Levy flight strategy is adopted to enhance the algorithm’s global search and local optimization capabilities,avoid premature algorithm,and ultimately achieve optimal results.This article selects three unimodal test functions and three multimodal test functions to perform preliminary tests on Particle Swarm optimization(PSO),Cuckoo Search method(CS),basic Snake Optimizer algorithm(SO),and Improved Snake Optimizer(ISO)to verify the optimization effect and convergence performance of the ISO algorithm.Finally,establish a photovoltaic simulation model using the ISO control algorithm,and distribute the battery modules in four situations: uniform illumination,local shading,sudden changes in illumination,and sudden changes in illumination intensity and temperature under local shading.Analyze and compare the ISO algorithm proposed in this article with PSO algorithm,CS algorithm,and basic SO algorithm to further verify the performance of the ISO algorithm.Simulation was found the ISO algorithm proposed in this article has better optimization ability.It can quickly and accurately find the maximum power point of photovoltaic cells under various environmental conditions and is easy to skip local optima.The convergence response time is less than 0.20 s,and the tracking power error is less than 1%.Therefore,the application of ISO algorithm in MPPT has great advantages,and this control algorithm can quickly adapt to environmental changes,has good stability,and effectively improves the power generation efficiency of photovoltaic systems.