Application Of Improved Particle Swarm Optimization Algorithm Based On Tent Chaotic Map In Photovoltaic MPPT

In today’s world,fossil fuels generate more than 60% of the total electricity.The energy crisis and environmental pollution have prompted people to look for a cleaner and more environmentally friendly way to generate electricity.Solar photovoltaic power generation is clean and environmentally friendly,and is an ideal power generation method.However,due to its low photoelectric conversion efficiency,the output power is greatly affected by light,and the power generation is unstable,the development of photovoltaic power generation is restricted.And the core problem is the maximum power point tracking(MPPT).Based on the analysis of the principle of photovoltaic cells,by setting the external environment,the equivalent circuit of photovoltaic cells and related mathematical models of photovoltaic cells are established to adapt to the external environment.For the convenience of engineering calculation,the mathematical model is simplified.In Matlab/Simulink,a simulation experimental model of photovoltaic array under partial shadow is established.Through the simulation experiment,the relationship between the output characteristics of photovoltaic cells and light and temperature is obtained,and the PU and IU curves of photovoltaic cells under different conditions are given.The simulation results show that the P-U curve of the photovoltaic array under local shadows is a multimodal curve.In fact,the P-U curve of the photovoltaic array is more complex due to the influence of the illumination temperature,which makes the output power control of the photovoltaic array more difficult.Traditional MPPT methods cannot track the maximum power point well in variable environment.Based on the MPPT methods,this paper introduces a particle swarm optimization algorithm with good optimization efficiency;analyzes the problems of the particle swarm optimization algorithm in MPPT,which are prone to premature convergence and falling into the local optimal solution.In order to solve the problem of premature convergence and falling into the local optimal solution in MPPT,tent chaotic mapping is introduced to keep the uniform distribution of the particle population during initialization.An improved particle swarm algorithm based on tent chaotic mapping is designed,which improves the quality of the particle population and reduces the accidental error.Under local shadows,the optimization results of perturbation observation method,standard particle swarm optimization method and improved particle swarm algorithm based on tent chaotic mapping are compared through simulation experiments,and the feasibility and validity of improved particle swarm algorithm based on tent chaotic mapping are verified.