摘要
当局部阴影出现时,光伏阵列的P-U输出曲线会出现多个峰值。为解决传统粒子群算法在光伏最大功率点追踪(MPPT)中出现的问题,提出了一种优化方法,在粒子群算法上采用遗传算法中的自然选择机理。该方法的主要思想是在每次迭代过程中对整个种群进行一次适应度值排序,并用种群中表现最好的那部分粒子的速度和位置替换种群最差的部分,这样可以提高搜索范围和全局寻优性能。同时,对学习因子和惯性权重进行优化调整,以提高算法收敛速度与精度。这种方法可以有效解决粒子群算法在光伏MPPT中响应速度慢、振荡明显、易陷入局部的问题。通过静态遮阴、动态遮阴突变情况下进行仿真分析,结果表明所提出的算法能够实现MPPT的精确控制,具有更快的收敛速度,更高的稳态精度。
When partial shading occurs,the P-U output curve of the photovoltaic array will exhibit multiple peaks.To solve the problems of slow response,significant oscillations,and susceptibility to local optima in traditional particle swarm optimization algorithms for maximum power point tracking(MPPT)in photovoltaic systems,an optimization method that combines the natural selection mechanism in genetic algorithms with particle swarm optimization is proposed.The core idea of this method is to sort all particles in the population by fitness in each iteration,and replace the worst half of the population with the best half in terms of ve⁃locity and position.This can improve the search range and global optimization performance.In addition,the learning factor and iner⁃tia weight are optimized and adjusted to improve the convergence speed and accuracy of the algorithm.This method can effectively solve the problems of slow response,significant oscillations,and susceptibility to local optima in particle swarm optimization algo⁃rithms for photovoltaic MPPT and has better optimization performance.To achieve more efficient photovoltaic MPPT,this method can be widely applied.
作者
郝武帮
施磊
HAO Wubang;SHI Lei(School of Electrical and Information Technology,Yunnan Minzu University,Kunming 650000)
出处
《舰船电子工程》
2024年第11期186-191,203,共7页
Ship Electronic Engineering
基金
国家自然科学基金项目(编号:62062068)资助。
关键词
光伏阵列
局部阴影
粒子群算法
自然选择
遗传算法
仿真分析
photovoltaic array
partial shading
particle swarm algorithm
natural selection
genetic algorithm
simulated analysis
