基于改进白骨顶鸡算法的局部荫蔽光伏MPPT
Local shaded photovoltaic MPPT based on improved coot chicken algorithm
瞿崎 1谭功全 2庞宏杰 3冯志强3
作者信息
- 1. 四川轻化工大学 自贡 643000
- 2. 四川轻化工大学 自贡 643000;人工智能四川省重点实验室 宜宾 644000
- 3. 人工智能四川省重点实验室 宜宾 644000
- 折叠
摘要
针对光伏发电系统在局部荫蔽下传统最大功率点追踪方法极易陷入局部最优而导致功率震荡范围较大等问题,提出一种基于改进白骨顶鸡算法的光伏MPPT方法.该算法在传统白骨顶鸡算法的基础上,将Logistic-Sine-Cosine混沌映射因子引入种群跟随者的链式移动中,从而使链式移动变为混沌移动,让算法具备跳出局部最优解的能力;对每次寻优结束后的当前最优位置进行柯西变异,对比变异前后择优更新替代,增加算法的全局搜索能力.在四种光照模式下,将ICOOT与另外三种算法的MPPT进行仿真分析.结果显示,所提改进算法的追踪速度为0.14 s,1.13 s,0.13 s,1.07 s,系统稳定率为99.43％,99.34％,98.73％,98.80％.综合来看,ICOOT在用于光伏发电局部隐蔽MPPT时能有效解决传统算法易于陷入局部最大功率点而导致寻优速度慢、功率震荡大的问题.
Abstract
Aiming at the problem that the traditional Maximum power point tracking method is easy to fall into local optimal and lead to large power oscillation range in photovoltaic power generation system under local shade,an improved algorithm based on Coot chicken was proposed.The photovoltaic MPPT method of ICOOT.Based on the traditional Coot bird optimization algorithm,the algorithm introduced the logistics-sine-cosine chaotic mapping factor into the chain movement of the population follower,so that the chain movement became chaotic movement.The algorithm has the ability to jump out of the local optimal solution;The current optimal position after each optimization is modified by Cauchy,and the optimal update and replacement before and after the optimization are compared to increase the global search ability of the algorithm.Under four lighting modes,ICOOT and MPPT of the other three algorithms were simulated and analyzed.The results show that the tracking speed of the proposed algorithm is 0.14 s,1.13 s,0.13 s,1.07 s,and the system stability rate is 99.43％,99.34％,98.73％,98.80％.In summary,ICOOT can effectively solve the problem that traditional algorithm is easy to fall into the local maximum power point,which leads to slow optimization speed and large power oscillation when used for local concealed MPPT of photovoltaic power generation.
关键词
最大功率点追踪/局部荫蔽/白骨顶鸡算法/混沌映射/柯西变异Key words
maximum power point tracking/local shading/COOT bird algorithm/chaos mapping/Cauchy mutation引用本文复制引用
基金项目
四川省科技厅项目(2020JDJQ0075)
人工智能四川省重点实验室科研项目(2019RYJ08)
出版年
2024
NETL
NSTL
万方数据