基于BSO的局部阴影下光伏最大功率点追踪

被引：33

作者：

赵帅旗 ^{[1
]}

肖辉 ^{[1
]}

刘忠兵 ^{[2
]}

朱梓嘉 ^{[1
]}

张万 ^{[1
]}

机构：

[1] 长沙理工大学电气与信息工程学院

[2] 湖南大学土木工程学院

来源：

电力系统及其自动化学报 | 2020年 / 32卷 / 06期

关键词：

局部阴影; 天牛须搜索; 天牛群优化; 最大功率点追踪; 粒子群优化;

D O I：

10.19635/j.cnki.csu-epsa.000336

中图分类号：

TM615 [太阳能发电]; TP18 [人工智能理论];

学科分类号：

080811 [新能源发电与电能存储]; 140502 [人工智能];

摘要：

为了减小局部遮阴情况PSC(partial shading condition)下光伏系统的功率失配损失,提高最大功率点追踪MPPT(maximum power point tracking)的追踪速度和准确性,提出了基于天牛群优化BSO(beetle swarm optimization)算法的MPPT控制方法。把由天牛须搜索BAS(beetle antennae search)借鉴粒子群的群体优化思想而得到的BSO方法应用到MPPT控制,利用天牛的个体进化和群体学习等优势来提高MPPT的追踪速度和精确度。设置了多种光照情况来作仿真验证,并用粒子群方法进行比较分析。结果表明,所提的方法追踪速度更快、精确度更高,且追踪过程更稳定、功率波动较小。

引用

页码：74 / 79+100 +100

页数：7

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