摘要
在局部遮阴或光照不均匀的情况下,光伏阵列的功率-电压(P-U)特性曲线会出现多个极值点。传统智能优化算法普遍存在收敛速度慢、精度低和易陷入局部最优等缺陷。为解决该问题,提出改进乌鸦算法(ICSA)的MPPT控制方法。在种群初始化上,引入基于反向学习的Tent混沌初始化策略,增加种群多样性,帮助跳出局部最优;在算法位置跟踪上,引入异花授粉策略与共享机制相结合更新乌鸦位置,提高算法收敛速度和精度。通过建模仿真,证明了改进乌鸦算法在复杂环境条件下跳出局部最优能力更强、具有更快的追踪速度和更高的精度。
In the case of partial shading or uneven illumination,the power-voltage(P-U)characteristic curve of the photovoltaic array will have multiple extreme points.The traditional intelligent optimization algorithms generally have defects such as slow convergence speed,low accuracy and falling into local optimum easily.To solve this problem,an improved crow algorithm(ICSA)MPPT control method was proposed.In the population initialization,the Tent chaotic initialization strategy based on reverse learning was introduced to increase the diversity of the population and help jump out of the local optimum;in the algorithm position tracking,the alienation pollination strategy and the sharing mechanism were combined to update the crow position and improve the algorithm convergence speed and precision.Through modeling and simulation,it is proved that the improved crow algorithm has stronger ability to jump out of the local optimum under complex environmental conditions,and has faster tracking speed and higher accuracy.
作者
袁建华
何宝林
赵子玮
李尚
刘宇
YUAN Jianhua;HE Baolin;ZHAO Ziwei;LI Shang;LIU Yu(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang Hubei 443000,China)
出处
《电源技术》
CAS
北大核心
2021年第7期915-918,944,共5页
Chinese Journal of Power Sources
基金
煤燃烧国家重点实验室开放基金项目(FSKLCCA1607)
梯级水电站运行与控制湖北省重点实验室基金项目(2015KJX07)
产学研协同培养研究生实践创新能力机制研究项目(SDYJ201604)。
关键词
光伏阵列
局部阴影
乌鸦算法
混沌初始化
反向学习
异花授粉
共享机制
PV array
partial shadow
crow algorithm
chaotic initialization
reverse learning
alienation pollination
sharing mechanism
