基于改进蜉蝣算法的光伏多峰值最大功率跟踪特性研究
RESEARCH ON PHOTOVOLTAIC MULTI-PEAK MAXIMUM POWER TRACKING CHARACTERISTICS BASED ON IMPROVED MAYFLY ALGORITHM
赵智勇 1李卫军 1郑秒 1朱运奇 1张翼1
作者信息
- 1. 国网河南省电力公司驻马店供电公司,驻马店 463000
- 折叠
摘要
使用一种改进蜉蝣算法(EMA)解决在局部阴影条件下,太阳电池P-U图呈现多峰值,传统最大功率点跟踪算法易陷入局部最优解,导致无法快速准确找到光伏最大功率跟踪点的问题.首先通过雄性蜉蝣的全局搜索,跳出局部最优解;然后利用雌性蜉蝣的随机局部搜索和交配,减少系统震荡;最后通过设置两组不同的算例,验证所使用算法的跟踪精度和跟踪速度.研究结果表明:与粒子群算法(PSO)和灰狼算法(GWO)相比,改进蜉蝣算法在搜索过程中可减少光伏输出功率震荡.相较于PSO和GWO,在EWA在跟踪速度上得到显著提升,使得太阳电池在局部阴影下仍能保持高效的输出功率.
Abstract
An improved mayfly algorithm(EMA)is used to solve the problem of multiple peaks in solar ceu P-U graphs under partial shading conditions,where traditional maximum power point tracking(MPPT)algorithms tend to fall into local optimal solution,making it difficult to quickly and accurately find the global maximum power point of the PV system.Firstly,the male mayflies perform a global search to escape local optimal solution.Then,the female mayflies reduce system oscillations through random local search and mating.Finally,two different scenarios are set to verify the tracking accuracy and speed of the proposed algorithm.The results show that compared with particle swarm optimization(PSO)and grey wolf optimization(GWO),the EMA reduces PV output power oscillations during the search process.Compared to PSO and GWO,EMA has significantly improved the tracking speed,allowing the solar cells to maintain high output power even under partial shading.
关键词
光伏发电/最大功率点跟踪/改进蜉蝣算法/局部阴影/多峰值/光伏输出特性Key words
PV generation/maximum power point tracking/improved Mayfly algorithm/partial shading/multi-peak/PV output characteristics引用本文复制引用
出版年
2024
NETL
NSTL
万方数据