局部阴影下基于GWO-P&Q混合算法的光伏最大功率点跟踪
Maximum photovoltaic power point tracking based on hybrid GWO-P&Q algorithm under local shadow
赵峰 1肖成锐 1陈小强 1王英1
作者信息
- 1. 兰州交通大学 自动化与电气工程学院,甘肃兰州 730070
- 折叠
摘要
针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、 后期收敛速度慢、 稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&Q)各自的优势,提出了基于GWO-P&Q的混合优化最大功率点跟踪(Maximum power point tracking,MPPT)算法.首先,采用灰狼优化算法逐渐向光伏的全局最大功率点靠近.其次,在灰狼优化算法收敛后期引入P&Q法,既保持了灰狼优化算法较高的稳态精度,又能以较快速度寻找到局部最大功率点.最后,在不同环境工况下,将所提出的GWO-P&Q方法与传统GWO算法进行对比.结果表明,改进的GWO-P&Q算法在保证良好稳态性能的同时,一定程度上提高了GWO算法后期跟踪最大功率时的收敛速度.
Abstract
In view of the problem of multiple peak values of P-U characteristic curve under a local shading environment,the traditional gray wolf optimization (GWO) is slow in convergence speed and low in accuracy of steady state at the late stage when tracking the maximum power point. Combining the advantages of GWO and perturbation & observation (P&O) method, an improved hybrid maximum power point tracking(MPPT) algorithm based on GWO-P&O was proposed. Firstly, optimized by the GWO, the algorithm was gradually close to the global MPPT. Then, P&O was introduced into the GWO at the late convergence stage, so that the local maximum power point of of photovoltaic power can be found at a faster speed while maintaining a high steady-state accuracy of the GWO, which overcomes the shortcomings of the traditional GWO algorithm. Finally, the proposed method was compared with the GWO under different environments. The results show that the proposed GWO-P&O method can improve the convergence speed in the late stage of the GWO when tracking the maximum power while ensuring high steady-state accuracy.
关键词
灰狼优化算法/扰动观察法/局部遮阴/混合优化最大功率点跟踪算法/全局最大功率点Key words
gray wolf optimization(GWO)/perturbation & observation(P&Q) method/partial shading environment/hybrid maximum power point tracking (MPPT) algorithm/global maximum power point引用本文复制引用
基金项目
National Natural Science Foundation of China(52067013)
Natural Science Foundation of Gansu Province(21JR7RA280)
出版年
2024
国家科技期刊平台
NSTL
万方数据