Research on Maximum Power Point Tracking Technology of Adaptive Particle Swarm Optimization Based on Greedy Strategy

With the increasing interest in renewable energy sources, photovoltaic power generation has emerged as a popular choice. The maximum power point tracking technology plays a crucial role in the efficient operation of photovoltaic systems. However, in scenarios involving partial shading, the solar array's power output demonstrates a complex multi-peak characteristic. When employing a standard method for monitoring peak power, one may inadvertently settle for a locally optimal value, rather than the global maximum. At the same time, the intelligent algorithm's adaptive particle swarm optimization algorithm has some tracking issues, such as large oscillation, low precision, and a long optimization time. This work proposes a control method for an adaptive particle swarm optimization algorithm based on a greedy algorithm to handle this challenge. The issues of standard adaptive particle swarm optimization methods are handled by incorporating the global optimal and local optimal particle difference, as well as the proportional coefficient of particle moving speed, and the solar energy consumption rate is enhanced further.