Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT
Photovoltaic power generation system is extremely sensitive to the change of illumination, when it is obscured by dust, cloud shadows, etc., it will produce a power voltage characteristic curve with multi-peak characteristics, and the traditional Maximum Power Point Tracking (MPPT) technology will f...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024021200 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850082879052185600 |
|---|---|
| author | ZiJian Zhou YanHong Fang |
| author_facet | ZiJian Zhou YanHong Fang |
| author_sort | ZiJian Zhou |
| collection | DOAJ |
| description | Photovoltaic power generation system is extremely sensitive to the change of illumination, when it is obscured by dust, cloud shadows, etc., it will produce a power voltage characteristic curve with multi-peak characteristics, and the traditional Maximum Power Point Tracking (MPPT) technology will fall into the local optimal solution when it meets multiple peaks. In order to solve this problem, the latest intelligent optimization algorithm, Eel and Grouper Optimizer (EGO), is used to integrate chaos mapping, Grasshopper Optimization Algorithm (GOA) and other improved methods on the basis of the original algorithm. Firstly, the chaotic mapping is used to initialize the distribution of particles to improve the search performance. After the EGO as the main algorithm completed the first position update, the GOA was used to redistribute the particles, and finally the tumbling formula was introduced to further improve the ability of particles to jump out of the local optimal solution. Finally, the data of simulation experiment proves that: In the three groups of light comparison experiments, the tracking efficiency reached 94.03 % in the constant light experiment, the tracking efficiency of the two-stage variable light experiment is more than 98 %, and the comprehensive tracking performance of the three-stage variable light experiment is the best compared with other algorithms, and no large power loss is generated in the tracking process, and the curve is relatively stable. By comparing the graph with the data, it shows that the improved Eel and Grouper Optimizer used in this paper has good performance and prospect in the application of Maximum Power Point Tracking. |
| format | Article |
| id | doaj-art-dd155bb5d10347de994495332685b321 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-dd155bb5d10347de994495332685b3212025-08-20T02:44:24ZengElsevierResults in Engineering2590-12302025-03-012510387710.1016/j.rineng.2024.103877Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPTZiJian Zhou0YanHong Fang1College of Electrical and Engineering Control, Liaoning Technical University, Xingcheng 125100, ChinaCorresponding author.; College of Electrical and Engineering Control, Liaoning Technical University, Xingcheng 125100, ChinaPhotovoltaic power generation system is extremely sensitive to the change of illumination, when it is obscured by dust, cloud shadows, etc., it will produce a power voltage characteristic curve with multi-peak characteristics, and the traditional Maximum Power Point Tracking (MPPT) technology will fall into the local optimal solution when it meets multiple peaks. In order to solve this problem, the latest intelligent optimization algorithm, Eel and Grouper Optimizer (EGO), is used to integrate chaos mapping, Grasshopper Optimization Algorithm (GOA) and other improved methods on the basis of the original algorithm. Firstly, the chaotic mapping is used to initialize the distribution of particles to improve the search performance. After the EGO as the main algorithm completed the first position update, the GOA was used to redistribute the particles, and finally the tumbling formula was introduced to further improve the ability of particles to jump out of the local optimal solution. Finally, the data of simulation experiment proves that: In the three groups of light comparison experiments, the tracking efficiency reached 94.03 % in the constant light experiment, the tracking efficiency of the two-stage variable light experiment is more than 98 %, and the comprehensive tracking performance of the three-stage variable light experiment is the best compared with other algorithms, and no large power loss is generated in the tracking process, and the curve is relatively stable. By comparing the graph with the data, it shows that the improved Eel and Grouper Optimizer used in this paper has good performance and prospect in the application of Maximum Power Point Tracking.http://www.sciencedirect.com/science/article/pii/S2590123024021200Eel and Grouper OptimizerGrasshopper Optimization AlgorithmChaotic mappingAdaptive parameterMaximum power point trackingPhotovoltaic power generation |
| spellingShingle | ZiJian Zhou YanHong Fang Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT Results in Engineering Eel and Grouper Optimizer Grasshopper Optimization Algorithm Chaotic mapping Adaptive parameter Maximum power point tracking Photovoltaic power generation |
| title | Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT |
| title_full | Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT |
| title_fullStr | Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT |
| title_full_unstemmed | Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT |
| title_short | Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT |
| title_sort | eel and grouper optimizer improvement three stage algorithm for photovoltaic mppt |
| topic | Eel and Grouper Optimizer Grasshopper Optimization Algorithm Chaotic mapping Adaptive parameter Maximum power point tracking Photovoltaic power generation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024021200 |
| work_keys_str_mv | AT zijianzhou eelandgrouperoptimizerimprovementthreestagealgorithmforphotovoltaicmppt AT yanhongfang eelandgrouperoptimizerimprovementthreestagealgorithmforphotovoltaicmppt |