光伏非线性变异环形粒子群MPPT算法

Photovoltaic nonlinear mutation ring particle swarm MPPT algorithm

为解决局部阴影条件下传统智能算法最大功率点跟踪(maximum power point tracking, MPPT)波动剧烈、收敛效果不佳等问题,采用个体位置变异环形粒子群(particle swarm optimization, PSO)优化策略,实现惯性权重与学习因子非线性变化,提高种群遍历性,均衡全局搜索与局部收敛能力,形成一种非线性变异改进环形粒子群(mutation improved ring particle swarm optimization, MIRPSO)MPPT新算法。将MIRPSO算法与传统PSO算法进行数字仿真对比与分析,实验结果表明:在多峰值寻优过程中,采用MIRPSO算法,最大功率追踪时间可缩短57.5%,追踪误差仅为2.5‰,功率振荡减弱,电能损失减小,在光照强度突变及温度持续变化时,同样可以重新准确定位到全局最大功率点。

In order to solve the problems of severe fluctuation and poor convergence effect of traditional intelligent algorithm maximum power point tracking( MPPT) under local shadow conditions, the particle swarm optimization( PSO) optimization strategy with individual position variation is adopted to realize the nonlinear change of inertia weight and learning factor, improve the population ergodicity and balance the global search and local convergence ability. A new MPPT algorithm of nonlinear mutation improved ring particle swarm optimization( MIRPSO) is formed. The comparison and analysis of digital simulation results between MIRPSO algorithm and traditional PSO algorithm show that in the process of multi-peak optimization, the maximum power tracking time is shortened by 57.5%, and the tracking error is only 2.5‰.The power oscillation is weakened, and the power loss is reduced. When the light intensity changes suddenly and the temperature changes continuously, the global maximum power point can also be relocated accurately.