基于粒子群遗传算法的光伏MPPT控制研究

局部阴影条件下，光伏阵列的功率特性曲线会出现多个峰值，传统的MPPT跟踪算法容易陷入局部极值点，无法准确地跟踪到最大功率点。粒子群算法具有很强的全局搜索能力，可以有效解决多峰寻优问题，但是普通粒子群算法容易出现收敛速度慢、早熟现象。提出一种改进的粒子群遗传（IPSO-GA）算法，该算法的惯性权重与学习因子随着迭代次数不断改变，可以同时兼顾算法的局部搜索与全局寻优能力，并且引进遗传算法的交叉、变异操作以增加种群多样性。仿真结果表明，改进算法在多峰最大功率跟踪过程中，具有良好的跟踪速度与寻优精度。

Research on PV MPPT control based on particle swarm genetic algorithm

Under the conditions of partial shading , many peaks will exhibited in the power characteristic curve of the PV array . The traditional MPPT methods are ineffective and are easy to fall into the local optimum. Particle swarm optimization can effectively solve the multi-peak problem by its strong global search ability, but the conventional PSO algorithm has a slow convergence rate and is easy to be precocious. An improved particle swarm genetic algorithm is proposed .The algorithm can balance the ability between local search and global optimization by changing the inertia weight and learning factors constantly. And use the crossover and mutation operation of the genetic algorithm to increase the diversity of the population. The simulation results show that improved algorithm has good tracking speed and precious in the process of multi-peak maximum power tracking.