SCSO: snake optimization with sine-cosine algorithm for parameter extraction of solar photovoltaic models
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| Publicado en: | SN Applied Sciences vol. 7, no. 4 (Apr 2025), p. 334 |
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| Publicado: |
Springer Nature B.V.
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| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2523-3963 | ||
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| 024 | 7 | |a 10.1007/s42452-025-06756-1 |2 doi | |
| 035 | |a 3190986028 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 245 | 1 | |a SCSO: snake optimization with sine-cosine algorithm for parameter extraction of solar photovoltaic models | |
| 260 | |b Springer Nature B.V. |c Apr 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Solar power generation is a clean and sustainable energy source. To ensure the efficient operation of photovoltaic (PV) systems, it is essential to develop accurate equivalent models of PV cells and precisely determine their unknown parameters. However, due to the nonlinear and multimodal characteristics of PV systems, accurately extracting PV parameters remains a significant challenge. This paper proposes a hybrid Snake Optimization combined with a Sine–Cosine Algorithm (SCSO) to address the PV parameter extraction problem. The proposed algorithm incorporates three key improvements: (1) integration of the Sine–Cosine Algorithm to enhance the bio-inspired Snake Optimization, balancing exploration and exploitation; (2)The parameters C1 and C2 are adaptively adjusted, and the Newton–Raphson method is introduced to accelerate the algorithm’s convergence speed which accelerates convergence; and (3) application of a lens imaging reverse learning strategy to improve exploration capabilities and population diversity, preventing the algorithm from becoming trapped in local optima. First, the performance of the SCSO algorithm is qualitatively analyzed using the CEC2022 test functions. Then, the algorithm is applied to extract parameters for three different PV modules. Finally, two commercial models (TFST 40 and MCSM 55) are tested under varying environmental conditions to validate the algorithm’s accuracy. Experimental results demonstrate that SCSO outperforms several state-of-the-art metaheuristic algorithms, achieving higher precision and faster convergence. Article Highlights<list list-type="bullet"><list-item></list-item>This paper proposes a new hybrid Snake Optimization combined with the Sine–Cosine Algorithm (SCSO) and conducts a qualitative analysis of the improved algorithm using CEC2022 test functions, demonstrating its superior performance.<list-item>The SCSO is applied to the extraction of unknown parameters in six solar photovoltaic module models, including the Single Diode Model (SDM), Double Diode Model (DDM), and PV module model.</list-item><list-item>Compared to other metaheuristic algorithms, the SCSO achieves faster and more precise parameter extraction, as demonstrated on two commercial PV models, TFST 40 and MCSM 55.</list-item> | |
| 653 | |a Algorithms | ||
| 653 | |a Clean technology | ||
| 653 | |a Cell culture | ||
| 653 | |a Sustainable energy | ||
| 653 | |a Trigonometric functions | ||
| 653 | |a Newton-Raphson method | ||
| 653 | |a Environmental conditions | ||
| 653 | |a Solar power generation | ||
| 653 | |a Clean energy | ||
| 653 | |a Feature selection | ||
| 653 | |a Energy sources | ||
| 653 | |a Convergence | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Photovoltaic cells | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Qualitative analysis | ||
| 653 | |a Photovoltaics | ||
| 653 | |a Fossil fuels | ||
| 653 | |a Optimization | ||
| 653 | |a Methods | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Parameters | ||
| 653 | |a Solar power | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 4 (Apr 2025), p. 334 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3190986028/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3190986028/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3190986028/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |