基于改进黏菌算法的局部遮阴下光伏MPPT研究

李红岩; 王磊; 安平娟; 杨朝旭; 赵天玥; 刘宝

doi:10.19912/j.0254-0096.tynxb.2022-0810

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (10) : 129-134. DOI: 10.19912/j.0254-0096.tynxb.2022-0810

基于改进黏菌算法的局部遮阴下光伏MPPT研究

李红岩, 王磊, 安平娟, 杨朝旭, 赵天玥, 刘宝

作者信息 +

STUDY ON PHOTOVOLTAIC MPPT UNDER LOCAL SHADE BASED ON IMPROVED SLIME MOLD ALGORITHM

Li Hongyan, Wang Lei, An Pingjuan, Yang Chaoxu, Zhao Tianyue, Liu Bao

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文章历史 +

摘要

为解决光伏发电系统在局部阴影光照情况下传统最大功率点跟踪（MPPT）方法极易陷入局部最优等问题,提出一种混沌变异黏菌算法结合算术算法（CVSMAAOA）的光伏MPPT方法。该方法在黏菌算法（SMA）基础上引入混沌映射函数初始化黏菌个体,使种群分布更加均匀;引入高斯变异策略提高算法跳出局部最优解的概率,变异因子的跃变性有助于加速搜索进程;引入算术优化算法（AOA）中加减算子实现局部高精度探索。仿真实验表明,所提算法相较SMA、AOA在均匀辐照、静态阴影、变化阴影下收敛速度具有明显提升。

Abstract

In order to solve the problem that the traditional maximum power point tracking （MPPT） method is easy to fall into local optimality in the case of local shadow illumination of photovoltaic power generation system, a photovoltaic MPPT method of mixed mutant slime mold algorithm combined with arithmetic algorithm （CVSMAAOA） is proposed. This method introduces a chaotic mapping function to initialize slime mold individuals on the basis of slime mold algorithm （SMA）, so that the population distribution is more uniform, the gaussian variation is introduced to improve the probability of the algorithm jumping out of the local optimal solution, and the jump of the variation factor helps to accelerate the search process; and the addition and subtraction operators in the arithmetic algorithm （AOA） are introduced to achieve local high-precision exploration. Simulation experiments show that compared with SMA and AOA, the convergence speed of the proposed algorithm under uniform irradiation, static shadow and changing shadow has been significantly improved.

导出引用

李红岩, 王磊, 安平娟, 杨朝旭, 赵天玥, 刘宝. 基于改进黏菌算法的局部遮阴下光伏MPPT研究[J]. 太阳能学报. 2023, 44(10): 129-134 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0810

Li Hongyan, Wang Lei, An Pingjuan, Yang Chaoxu, Zhao Tianyue, Liu Bao. STUDY ON PHOTOVOLTAIC MPPT UNDER LOCAL SHADE BASED ON IMPROVED SLIME MOLD ALGORITHM[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 129-134 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0810

中图分类号： TP18 TM615

参考文献

[1] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
LAI C W, LI J H, CHEN B, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[2] 肖文波, 余晓鹏, 张华明, 等. 遮荫下光伏发电数学模型的对比研究[J]. 电测与仪表, 2019, 56(10): 56-61.
XIAO W B, YU X P, ZHANG H M, et al.Comparative study of photovoltaic power generation math model under partial shading conditions[J]. Electrical measurement & instrumentation, 2019, 56(10): 56-61.
[3] 花赟昊, 朱武, 靳一奇, 等. 基于自适应变异粒子群算法的光伏MPPT控制研究[J]. 太阳能学报, 2022, 43(4): 219-225.
HUA Y H, ZHU W, JIN Y Q, et al.Research on photovoltaic MPPT control based on adaptive mutation particle swarm optimization algorithm[J]. Acta energiae solaris sinica, 2022, 43(4): 219-225.
[4] 叶国敏, 肖文波, 章文龙. 粒子群组合算法跟踪局部遮荫下光伏GMPPT研究[J]. 控制工程, 2022, 29(5): 910-917.
YE G M, XIAO W B, ZHANG W L.Research on PSO combined algorithm for tracking photovoltaic GMPPT under partial shading[J]. Control engineering of China, 2022, 29(5): 910-917.
[5] 刘春娟, 郑丽君, 孙赟赟, 等. 基于改进型细菌觅食算法的MPPT[J]. 太阳能学报, 2021, 42(9): 83-89.
LIU C J, ZHENG L J, SUN Y Y, et al.Maximum power point tracking strategy based on improved bacterial foraging algorithm[J]. Acta energiae solaris sinica, 2021, 42(9): 83-89.
[6] 石季英, 张登雨, 薛飞, 等. 基于改进灰狼优化-黄金分割混合算法的光伏阵列MPPT方法[J]. 电力系统及其自动化学报, 2019, 31(5): 21-26.
SHI J Y, ZHANG D Y, XUE F, et al.Maximum power point tracking method for photovoltaic array based on modified hybrid method of grey wolf optimization and golden-section optimization[J]. Proceedings of the CSU-EPSA, 2019, 31(5): 21-26.
[7] 薛飞, 马鑫, 田蓓, 等. 基于改进蜻蜓算法的光伏全局最大功率追踪[J]. 中国电力, 2022, 55(2): 131-137.
XUE F, MA X, TIAN B, et al.Photovoltaic global maximum power tracking based on improved dragonfly algorithm[J]. Electric power, 2022, 55(2): 131-137.
[8] 刘宜罡, 邹应全, 张晓强, 等. 基于差分进化的光伏MPPT算法改进[J]. 太阳能学报, 2020, 41(6): 264-271.
LIU Y G, ZOU Y Q, ZHANG X Q, et al.An improved photovoltaic MPPT algorithm based on differential evolution algorithm[J]. Acta energiae solaris sinica, 2020, 41(6): 264-271.
[9] 张晓强, 刘宜罡, 邹应全, 等. 基于自适应神经网络控制的光伏MPPT算法改进[J]. 太阳能学报, 2019, 40(11): 3095-3102.
ZHANG X Q, LIU Y G, ZOU Y Q, et al.An enhanced photovoltaic MPPT approach based on adaptive neural network control[J]. Acta energiae solaris sinica, 2019, 40(11): 3095-3102.
[10] LI S M, CHEN H L, WANG M J, et al.Slime mould algorithm: a new ethod for stochastic optimization[J]. Future generation computer systems, 2020, 111: 300-323.
[11] 贾鹤鸣, 刘宇翔, 刘庆鑫, 等. 融合随机反向学习的黏菌与算术混合优化算法[J]. 计算机科学与探索, 2022, 16(5): 1182-1192.
JIA H M, LIU Y X, LIU Q X, et al.Hybrid algorithm of slime mould algorithm and arithmetic optimization algorithm based on random opposition-based learning[J]. Journal of frontiers of computer science and technology, 2022, 16(5): 1182-1192.
[12] 刘宇凇, 刘升. 无迹西格玛点引导的拟反向黏菌算法及其工程应用[J]. 计算机应用研究, 2022, 39(9): 2709-2716.
LIU Y S, LIU S.Unscented sigma point guided quasi-opposite slime mould algorithm and its application in engineering problem[J]. Application research of computers, 2022, 39(9): 2709-2716.
[13] 肖亚宁, 孙雪, 李三平, 等. 基于混沌精英黏菌算法的无刷直流电机转速控制[J]. 科学技术与工程, 2021, 21(28): 12130-12138.
XIAO Y N, SUN X, LI S P, et al.Speed control of brushless direct current motor based on chaotic elite slime mould algorithm[J]. Science technology and engineering, 2021, 21(28): 12130-12138.
[14] 李志军, 张奕楠, 王丽娟, 等. 基于改进量子粒子群算法的光伏多峰MPPT研究[J]. 太阳能学报, 2021, 42(5): 221-229.
LI Z J, ZHANG Y N, WANG L J, et al.Study of photovoltaic multimodal maximum power point tracking based on improved quantum particle swarm optmization[J]. Acta energiae solaris sinica, 2021, 42(5): 221-229.
[15] ABUALIGAH L,DIABAT A, MIRJALILI S, et al.The arithmetic optimization algorithm[J]. Computer methods in applied mechanics and engineering, 2021, 376: 113609.
[16] 魏昕, 冯锋. 基于高斯-柯西变异的帝国竞争算法优化[J]. 计算机科学, 2021, 48(S2): 142-146.
WEI X, FENG F.Optimization of empire competition algorithm based on Gauss-Cauchy mutation[J]. Computer science, 2021, 48(S2): 142-146.