Efficient parameter extraction in PV solar modules with the diligent crow search algorithm
Abstract In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplic...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Discover Energy |
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| Online Access: | https://doi.org/10.1007/s43937-024-00063-3 |
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| author | Mostafa Jabari Morteza Azimi Nasab Mohammad Zand Lilia Tightiz Sanjeevikumar Padmanaban Juan C. Vasquez Q |
| author_facet | Mostafa Jabari Morteza Azimi Nasab Mohammad Zand Lilia Tightiz Sanjeevikumar Padmanaban Juan C. Vasquez Q |
| author_sort | Mostafa Jabari |
| collection | DOAJ |
| description | Abstract In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplicity in calculations, which collectively accelerate convergence to the global minimum and enhance the algorithm's robustness. Notably, it effectively avoids local minima, ensuring the algorithm does not become trapped. Furthermore, this method eliminates the need for developing new metacognitive algorithms. To demonstrate its benefits, we apply this method to the crow search optimization algorithm (CSA), which is notably deficient in convergence speed, robustness, stability, and escaping local minima. Consequently, the enhanced algorithm is termed the diligent crow search optimization algorithm (DCSA). Additionally, we utilize the powerful DCSA algorithm to identify the parameters of solar cells, aiming to maximize power output from solar energy—a critical global concern. To evaluate the proposed algorithm, we tested it on various solar cell models, including one-diode, two-diode, and three-diode configurations, as well as several widely used solar panels such as SM55, KC200GT, and SW255. We also examined the impacts of radiation, temperature, and unknown parameters on these solar panels. The simulation results demonstrate that implementing the proposed method on the crow algorithm resulted in a 98% improvement in stability and a sevenfold increase in convergence speed. |
| format | Article |
| id | doaj-art-a40322e03f854fd8af98796c5a879d2c |
| institution | Kabale University |
| issn | 2730-7719 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Energy |
| spelling | doaj-art-a40322e03f854fd8af98796c5a879d2c2025-01-05T12:50:35ZengSpringerDiscover Energy2730-77192024-12-014112910.1007/s43937-024-00063-3Efficient parameter extraction in PV solar modules with the diligent crow search algorithmMostafa Jabari0Morteza Azimi Nasab1Mohammad Zand2Lilia Tightiz3Sanjeevikumar Padmanaban4Juan C. Vasquez Q5Faculty of Electrical Engineering, Sahand University of Technology TabrizDepartment of Electrical Engineering, Information Technology and Cybernetic, University of South-Eastern NorwayDepartment of Electrical Engineering, Information Technology and Cybernetic, University of South-Eastern NorwaySchool of Computing, Gachon UniversityDepartment of Electrical Engineering, Information Technology and Cybernetic, University of South-Eastern NorwayCenter of Reliable Power Electronics (CoRPE), Energy Engineering, Aalborg UniversityAbstract In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplicity in calculations, which collectively accelerate convergence to the global minimum and enhance the algorithm's robustness. Notably, it effectively avoids local minima, ensuring the algorithm does not become trapped. Furthermore, this method eliminates the need for developing new metacognitive algorithms. To demonstrate its benefits, we apply this method to the crow search optimization algorithm (CSA), which is notably deficient in convergence speed, robustness, stability, and escaping local minima. Consequently, the enhanced algorithm is termed the diligent crow search optimization algorithm (DCSA). Additionally, we utilize the powerful DCSA algorithm to identify the parameters of solar cells, aiming to maximize power output from solar energy—a critical global concern. To evaluate the proposed algorithm, we tested it on various solar cell models, including one-diode, two-diode, and three-diode configurations, as well as several widely used solar panels such as SM55, KC200GT, and SW255. We also examined the impacts of radiation, temperature, and unknown parameters on these solar panels. The simulation results demonstrate that implementing the proposed method on the crow algorithm resulted in a 98% improvement in stability and a sevenfold increase in convergence speed.https://doi.org/10.1007/s43937-024-00063-3PhotovoltaicOptimizationRenewable energyCrow search optimization algorithm |
| spellingShingle | Mostafa Jabari Morteza Azimi Nasab Mohammad Zand Lilia Tightiz Sanjeevikumar Padmanaban Juan C. Vasquez Q Efficient parameter extraction in PV solar modules with the diligent crow search algorithm Discover Energy Photovoltaic Optimization Renewable energy Crow search optimization algorithm |
| title | Efficient parameter extraction in PV solar modules with the diligent crow search algorithm |
| title_full | Efficient parameter extraction in PV solar modules with the diligent crow search algorithm |
| title_fullStr | Efficient parameter extraction in PV solar modules with the diligent crow search algorithm |
| title_full_unstemmed | Efficient parameter extraction in PV solar modules with the diligent crow search algorithm |
| title_short | Efficient parameter extraction in PV solar modules with the diligent crow search algorithm |
| title_sort | efficient parameter extraction in pv solar modules with the diligent crow search algorithm |
| topic | Photovoltaic Optimization Renewable energy Crow search optimization algorithm |
| url | https://doi.org/10.1007/s43937-024-00063-3 |
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