Guided hybrid meta-intelligence for advanced photovoltaic parameter estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10258890" target="_blank" >RIV/61989100:27730/25:10258890 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352484725005438" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352484725005438</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.egyr.2025.09.022" target="_blank" >10.1016/j.egyr.2025.09.022</a>

Result language
angličtina
Original language name
Guided hybrid meta-intelligence for advanced photovoltaic parameter estimation
Original language description
The growing demand for sustainable energy has intensified the need for accurate photovoltaic (PV) system modeling, which critically depends on reliable parameter estimation. However, due to the nonlinear and multimodal nature of PV models, this task remains a significant challenge. To address this, we introduce a novel hybrid optimization algorithm-EDE-GWO-NR-that integrates Enhanced Differential Evolution (EDE) for global exploration, Grey Wolf Optimizer (GWO) for local exploitation, and Newton-Raphson (NR) for precise solution refinement. This hybrid approach effectively balances exploration and exploitation, leading to faster convergence and higher accuracy. Extensive experiments on various PV technologies, under both variable temperature and irradiance conditions, demonstrate the algorithm's superior performance in minimizing Root Mean Square Error (RMSE) compared to ten state-of-the-art metaheuristic methods. For example, on the RTC France solar cell dataset, EDE-GWO-NR achieved an RMSE of 7.75 xE-04, outperforming other methods such as GWO (10.08 xE-04) and PSO (9.86 xE-04). The results highlight EDE-GWO-NR's robustness, adaptability, and practical applicability for real-world PV parameter identification tasks.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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