Guided hybrid meta-intelligence for advanced photovoltaic parameter estimation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Guided hybrid meta-intelligence for advanced photovoltaic parameter estimation

Popis výsledku v původním jazyce

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&apos;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&apos;s robustness, adaptability, and practical applicability for real-world PV parameter identification tasks.

Název v anglickém jazyce

Guided hybrid meta-intelligence for advanced photovoltaic parameter estimation

Popis výsledku anglicky

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&apos;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&apos;s robustness, adaptability, and practical applicability for real-world PV parameter identification tasks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Reports

ISSN

2352-4847

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

1-20

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

20

Strana od-do

2607-2626

Kód UT WoS článku

001584810300001

EID výsledku v databázi Scopus

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