Research on Maximum Power Tracking Method of Photovoltaic Power Generation System
To improve the existing photovoltaic(PV)system which is easy to fall into local optimization when tracking the maximum power,an improved meme reinforcement learning(RL)model is proposed.Firstly,based on analyzing the equivalent circuit of the PV system,a power generation model of the PV system under partial shading conditions is established.Secondly,an optimization search model combining meme and RL is proposed,which can quickly search for high-quality optimal values based on the updated feedback rewards to approximate the global maximum power tracking of the PV system.Finally,a multi-group meme optimization exploration and exploitation strategy is used in the improved meme RL model to effectively search for both local optimal and global optimal solutions.The meteorological data of a city is used as an example to simulate the proposed model.The simulation results show that the proposed model has the lowest average variability,indicating that the model is robust.In addition,the proposed model produces the highest output energy in winter,indicating that the model can effectively track the maximum power of PV,which verifies the stability of the model.
Smart gridPhotovoltaic(PV)Maximum power trackingReinforcement learning(RL)modelMemeOptimization
NETL
NSTL
万方数据
维普
