RESEARCH ON PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING BASED ON IMPROVED PSO-LGWO OPTIMIZATION

Wang Yulin; Sun Liying

doi:10.19912/j.0254-0096.tynxb.2023-1775

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

RESEARCH ON PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING BASED ON IMPROVED PSO-LGWO OPTIMIZATION

更新时间：2025-10-18

- RESEARCH ON PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING BASED ON IMPROVED PSO-LGWO OPTIMIZATION
- Vol. 46, Issue 3, Pages: 328-334(2025)
- 作者机构：
  
  辽宁工业大学电气工程学院,锦州 121001
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1775
  CLC： TM615
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
王钰霖, 孙丽颖. 基于改进PSO-LGWO算法的光伏最大功率踪研究[J]. 太阳能学报, 2025, 46(3): 328-334.

Wang Yulin, Sun Liying. RESEARCH ON PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING BASED ON IMPROVED PSO-LGWO OPTIMIZATION[J]. 2025, 46(3): 328-334.
王钰霖, 孙丽颖. 基于改进PSO-LGWO算法的光伏最大功率踪研究[J]. 太阳能学报, 2025, 46(3): 328-334. DOI： 10.19912/j.0254-0096.tynxb.2023-1775.

Wang Yulin, Sun Liying. RESEARCH ON PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING BASED ON IMPROVED PSO-LGWO OPTIMIZATION[J]. 2025, 46(3): 328-334. DOI： 10.19912/j.0254-0096.tynxb.2023-1775.

摘要

在光伏阵列受到不均匀太阳辐照时

其输出特性曲线会出现多个峰值点

常规的最大功率点跟踪方法（MPPT）可能会陷入局部峰值点

导致光伏阵列不能在最大功率点下运行。为解决此类问题

提出一种基于改进粒子群优化的灰狼算法与莱维飞行模块相结合的算法（PSO-LGWO）。该算法在函数测试和静态阴影测试中

相较于其他灰狼算法都可在保证算法跟踪精度的同时提升收敛速度;在动态阴影测试中

相较于实际光伏发电站中常见的MPPT方法

可以跳出局部最优解

且在太阳辐照度变化较大时

在保证算法跟踪精度的同时具有更快的收敛速度。

Abstract

When the photovoltaic array is subjected to uneven solar irradiation

its output characteristic curve will have multiple peak points. The conventional maximum power point tracking （MPPT） method may fall into the local peak point

resulting in that the photovoltaic array cannot operate at the maximum power point. In order to solve such problems

an algorithm based on improved particle swarm optimization combined with grey wolf algorithm and Levy flight module （PSO-LGWO） is proposed. In the function test and static shadow test

the algorithm can improve the convergence speed while ensuring the tracking accuracy of the algorithm compared with other grey wolf algorithms. In the dynamic shadow test

compared with the common MPPT method in the actual photovoltaic power station

it can jump out of the local optimal solution. When the solar irradiance changes greatly

it has faster convergence speed while ensuring the tracking accuracy of the algorithm.

关键词

Keywords

references

范瑞祥, 尹国明, 苗洁蓉, 等. 基于参数辨识的光伏组件快速MPPT方法[J]. 太阳能学报, 2020, 41(2): 296-302.

花赟昊, 朱武, 郭启明. 光伏发电系统MPPT算法研究综述[J]. 电源技术, 2020, 44(12): 1855-1858.

ELGENDY M A, ZAHAWI B, ATKINSON D J.Operating characteristics of the P&O algorithm at high perturbation frequencies for standalone PV systems[J]. IEEE transactions on energy conversion, 2015, 30(1): 189-198.

ALI ELGENDY M, ATKINSON D J, ZAHAWI B.Experimental investigation of the incremental conductance maximum power point tracking algorithm at high perturbation rates[J]. IET renewable power generation, 2016, 10(2): 133-139.

聂晓华, 赖家俊. 局部阴影下光伏阵列全局最大功率点跟踪控制方法综述[J]. 电网技术, 2014, 38(12): 3279-3285.

PEDEMONTE M, NESMACHNOW S, CANCELA H.A survey on parallel ant colony optimization[J]. Applied soft computing, 2011, 11(8): 5181-5197.

ANBARASI M P, KANTHALAKSHMI S.Power maximization in standalone photovoltaic system: an adaptive PSO approach[J]. Soft computing, 2023, 27(12): 8223-8232.

TITRI S, LARBES C, TOUMI K Y, et al.A new MPPT controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions[J]. Applied soft computing, 2017, 58: 465-479.

MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.

葛传九, 武鹏, 董祥祥, 等. 基于布谷鸟算法的光伏MPPT改进[J]. 太阳能学报, 2022, 43(10): 59-64.

韩思鹏, 蒋晓艳, 罗意, 等. 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.

王加健, 帕孜来·马合木提, 孔博龙. 基于改进樽海鞘群算法的光伏系统MPPT研究[J]. 太阳能学报, 2022, 43(4): 191-197.

赵斌, 袁清, 王力, 等. 基于改进蚁狮算法的光伏多峰值MPPT控制[J]. 太阳能学报, 2021, 42(9): 132-139.

CHTITA S, MOTAHHIR S, EL HAMMOUMI A, et al.A novel hybrid GWO-PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions[J]. Scientific reports, 2022, 12(1): 10637.

毛明轩, 许钊, 崔立闯, 等. 基于改进灰狼优化算法的光伏阵列多峰MPPT研究[J]. 太阳能学报, 2023, 44(3): 450-456.

王红艳, 胡冠中, 方如举. 基于简化PSO算法和扰动观察法的光伏MPPT控制[J]. 电源技术, 2023, 47(8): 1095-1098.

HAKLı H, UĞUZ H. A novel particle swarm optimization algorithm with Levy flight[J]. Applied soft computing, 2014, 23: 333-345.

HEIDARI A A, PAHLAVANI P.An efficient modified grey wolf optimizer with Lévy flight for optimization tasks[J]. Applied soft computing, 2017, 60: 115-134.

吴玲, 张秀锦, 刘秋华, 等. 基于多元宇宙优化算法的光伏发电MPPT控制算法[J]. 太阳能学报, 2023, 44(9): 204-211.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

SLIDING MODE VARIABLE STRUCTURE CONTROL STRATEGY OF PV MPPT BASED ONI-VCHARACTERISTICS

RESEARCH ON MULTI-PEAK MPPT OF PHOTOVOLTAIC ARRAY BASED ON MODIFIED GRAY WOLF OPTIMIZATION ALGORITHM

MPPT ALGORITHM FOR PHOTOVOLTAICS BASED ON GA-GRU NEURAL NETWORK

RESEARCH ON SOLAR TRACKING TECHNOLOGY OF SOLAR CELL BASED ON SHAPE MEMORY ALLOY MODULATION

Related Author

Sun Tao

Ge Qiang

Li Juan

Wang Lu

Wang Bozhao

Mao Mingxuan

Xu Zhao

Cui Lichuang

Related Institution

College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, China

State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United Kingdom

State Grid Zhejiang Electric Power CO., Shaoxing Power Supply Company

School of Computer Science and Technology, Zhejiang University of Technology

AI问答

⁰