遮阴条件下光伏MPPT自适应粒子群算法优化

doi:10.19912/j.0254-0096.tynxb.2020-1001

太阳能学报 ›› 2022, Vol. 43 ›› Issue (6): 99-105.DOI: 10.19912/j.0254-0096.tynxb.2020-1001

遮阴条件下光伏MPPT自适应粒子群算法优化

韩思鹏¹, 蒋晓艳², 罗意³, 焦乾致¹, 谭景洋¹, 荀悦¹

1.西藏农牧学院水利土木工程学院,林芝 860000;
2.西藏农牧学院电气工程学院,林芝 860000;
3.西南交通大学电气工程学院,成都 611756

收稿日期:2020-09-17 出版日期:2022-06-28 发布日期:2022-12-28
通讯作者: 蒋晓艳（1973—）,女,硕士、教授,主要从事新能源应用与高电压绝缘方面的研究。Lz_jxy@163.com
基金资助:
2020年西藏自治区厅校联合自然科学基金（XZ202101ZR0107G）; 西藏自治区自然科学基金（XZ2019ZRG-54（Z））; 西藏农牧学院研究生创新计划（YJS2019-08）

PHOTOVOLTAIC MPPT ADAPTIVE PARTICLE SWARM OPTIMIZATION OPTIMIZATION UNDER SHADING CONDITIONS

Han Sipeng¹, Jiang Xiaoyan², Luo Yi³, Jiao Qianzhi¹, Tan Jingyang¹, Xun Yue¹

1. School of Water Conservancy and Civil Engineering, Tibet University of Agriculture and Animal Husbandry, Nyingchi 860000, China;
2. School of Electrical Engineering, Tibet University of Agriculture and Animal Husbandry, Nyingchi 860000, China;
3. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China

Received:2020-09-17 Online:2022-06-28 Published:2022-12-28

摘要/Abstract

摘要： 针对在局部阴影情况下光伏阵列的功率-电压（P-U）特性曲线呈多峰特性,粒子群算法应用于局部阴影下的最大功率点跟踪（MPPT）跟踪,存在搜索速度慢、精度低的缺点。提出自适应惯性权重粒子群优化（PSO）算法的最大功率点跟踪算法,自动更新惯性权重w和学习因子C₁、C₂,通过仿真实验,优化前的全局最大功率点（GMPP）跟踪时间是0.045 s,输出功率为468 W。优化后的自适应粒子群算法GMPP跟踪时间为0.020 s,输出功率稳定在为480 W,光伏阵列的输出功率跟踪误差小于30%。在所搭建辐照度突变模型仿真中,在4.022 s突变到300 W/m²时经过0.05 s又重新跟踪到了新的最大功率点稳定在0.075 MW。最后通过实验平台验证,优化后的自适应粒子群优化算法与传统的粒子群优化算法相比,追踪时间减少了55.5%,误差小于5%,验证了该算法可行性和实用性。

关键词: 粒子群优化, 部分遮阴, 最大功率点跟踪, 自适应惯性权重, 光伏阵列

Abstract: In view of the multi-peak characteristic of the power-voltage characteristic curve of photovoltaic array in the case of local shadow, particle swarm optimization algorithm is applied to MPPT tracking in the case of local shadow, which has the disadvantages of slow search speed and low accuracy. The maximum power point tracking algorithm of adaptive inertia weight particle swarm optimization algorithm is proposed to automatically update the inertia weights w and learning factors C₁ and C₂. Through simulation experiments, the GMPP time before optimization is 0.045 s and the output power is 468 W. After optimization, the GMPP time of the adaptive particle swarm algorithm is 0.02 s and the output power is stabilized at 480 W. The output power tracking error of the PV array is less than 30%. In the simulation by the irradiance mutation model established in this paper, the tracking time is reduced by 55.5% and the tracking error is less than 5% compared with the traditional particle swarm algorithm. The feasibility and practicability of the algorithm are verified.

Key words: PSO, partial shading, MPPT, adaptive inertia weight, PV array

中图分类号:

TM615

韩思鹏, 蒋晓艳, 罗意, 焦乾致, 谭景洋, 荀悦. 遮阴条件下光伏MPPT自适应粒子群算法优化[J]. 太阳能学报, 2022, 43(6): 99-105.

Han Sipeng, Jiang Xiaoyan, Luo Yi, Jiao Qianzhi, Tan Jingyang, Xun Yue. PHOTOVOLTAIC MPPT ADAPTIVE PARTICLE SWARM OPTIMIZATION OPTIMIZATION UNDER SHADING CONDITIONS[J]. Acta Energiae Solaris Sinica, 2022, 43(6): 99-105.

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遮阴条件下光伏MPPT自适应粒子群算法优化

PHOTOVOLTAIC MPPT ADAPTIVE PARTICLE SWARM OPTIMIZATION OPTIMIZATION UNDER SHADING CONDITIONS

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