Photovoltaic Maximum Power Point Tracking Technology Based on Improved Multi‐Subgroup Parallel Optimization Algorithm with Shuffled Frog Leaping Algorithm

To enhance the power generation efficiency of the photovoltaic system, it is necessary to ensure that it can operate stably at the global maximum power point (MPP). This paper presents a study on the maximum power point tracking(MPPT) technology of photovoltaic system and a new MPPT technique based on the improved multi‐subgroup parallel optimization algorithm with shuffled frog leaping algorithm (IMSPO‐SFLA) is proposed, which combines the improved multi‐subgroup parallel optimization algorithm with the shuffled frog leaping algorithm. Moreover, the developed MPPT technique improves the tracking accuracy of the MPP, increased the speed of MPP tracking, the problem of the traditional frog leaping algorithm being prone to falling into a local optimum is successfully overcame by it. In order to verify the effectiveness of the method, a simulation model is established in MATLAB in this paper. The Simulation results show that the method proposed in this paper can make the photovoltaic power generation system be operated more efficiently, which contributes to the development of the renewable energy field and promotes the progress of clean energy technology. Compared with the traditional algorithm, it has obvious advantages in realizing the MPPT of PV power generation system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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Publication Details

Journal
IEEJ Transactions on Electrical and Electronic Engineering
Published
2024-10-09
DOI
https://doi.org/10.1002/tee.24217
Citations
3
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.31
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article

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Multi‐Subgroup Parallel Optimization Algorithm with Shuffled Frog Leaping Algorithm

Di Tong, Jiqiu Nai, Shuang Chen, Zhen Guo et al.
3 citations
IEEJ Transactions on Electrical and Electronic Engineering
Photovoltaic System Optimization Techniques
0.31
article

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Multi‐Subgroup Parallel Optimization Algorithm with Shuffled Frog Leaping Algorithm

Di Tong, Jiqiu Nai, Shuang Chen, Zhen Guo, Minghao Ye, Shu Wang
article en
3 citations

Abstract

To enhance the power generation efficiency of the photovoltaic system, it is necessary to ensure that it can operate stably at the global maximum power point (MPP). This paper presents a study on the maximum power point tracking(MPPT) technology of photovoltaic system and a new MPPT technique based on the improved multi‐subgroup parallel optimization algorithm with shuffled frog leaping algorithm (IMSPO‐SFLA) is proposed, which combines the improved multi‐subgroup parallel optimization algorithm with the shuffled frog leaping algorithm. Moreover, the developed MPPT technique improves the tracking accuracy of the MPP, increased the speed of MPP tracking, the problem of the traditional frog leaping algorithm being prone to falling into a local optimum is successfully overcame by it. In order to verify the effectiveness of the method, a simulation model is established in MATLAB in this paper. The Simulation results show that the method proposed in this paper can make the photovoltaic power generation system be operated more efficiently, which contributes to the development of the renewable energy field and promotes the progress of clean energy technology. Compared with the traditional algorithm, it has obvious advantages in realizing the MPPT of PV power generation system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

IEEJ Transactions on Electrical and Electronic EngineeringVol. 20(4)
Qingdao University of Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 52%
Photovoltaic System Optimization Techniques
0.31
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Photovoltaic Maximum Power Point Tracking Technology Based on Improved Multi‐Subgroup Parallel Optimization Algorithm with Shuffled Frog Leaping Algorithm — Di Tong, Jiqiu Nai, et al. · IEEJ Transactions on Electrical and Electronic Engineering (2024) | TGRS Research Map | TGRS