Parameter Extraction of Photovoltaic Cell and Module with Four Diode Model Using Flood Algorithm DOI Creative Commons
İpek Çetinbaş

Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 5, 2024

Photovoltaic (PV) cells exhibit a nonlinear characteristic. Before modeling these cells, obtaining accurate parameters is essential. During the phase, using crucial for accurately characterizing and reflecting behavior of PV structures. Therefore, this article focuses on parameter extraction. A cell module were selected modeled four-diode model (FDM). This problem, consisting eleven unknown related to FDM, was solved with flood algorithm (FLA). To compare algorithm’s performance same polar lights optimizer (PLO), moss growth optimization (MGO), walrus (WO), educational competition (ECO) also employed. These five metaheuristic algorithms used first time in study, both solving extraction problem FDM. The objective function aimed at smallest root mean square error (RMSE) evaluated compared through assessment metrics, computational accuracy, time, statistical methods. minimum RMSE obtained FLA, calculated as 9.8251385E-04 FDM-C 1.6884311E-03 FDM-M. statistically demonstrate reinforce FLA’s success over other algorithms, Friedman test Wilcoxon signed-rank utilized. According tests, FLA produced significantly better results than outperformed them pairwise comparisons. In conclusion, has proven be successful promising extraction, its validated.

Language: Английский

Self-adaptive single-diode model parameter identification under small mismatching conditions DOI Creative Commons
Luis Enrique Garcia Marrero, Carlos I. Pavon-Vargas, Juan David Bastidas‐Rodríguez

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: 245, P. 122735 - 122735

Published: March 1, 2025

Language: Английский

Citations

0
Parameters identification of photovoltaic cell and module models based on the CSAO algorithm DOI
Yiping Xiao, Haiyang Zhang, Honghao Wei

et al.

Journal of Computational Electronics, Journal Year: 2025, Volume and Issue: 24(3)

Published: April 5, 2025

Language: Английский

Citations

0
Diversity enhancement-based Differential Evolution with a novel perturbation strategy DOI

Zhenghao Song,

Liangliang Sun,

Natalja M. Matsveichuk

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 92, P. 101822 - 101822

Published: Dec. 24, 2024

Language: Английский

Citations

0
Parameter Extraction of Photovoltaic Cell and Module with Four Diode Model Using Flood Algorithm DOI Creative Commons
İpek Çetinbaş

Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 5, 2024

Photovoltaic (PV) cells exhibit a nonlinear characteristic. Before modeling these cells, obtaining accurate parameters is essential. During the phase, using crucial for accurately characterizing and reflecting behavior of PV structures. Therefore, this article focuses on parameter extraction. A cell module were selected modeled four-diode model (FDM). This problem, consisting eleven unknown related to FDM, was solved with flood algorithm (FLA). To compare algorithm’s performance same polar lights optimizer (PLO), moss growth optimization (MGO), walrus (WO), educational competition (ECO) also employed. These five metaheuristic algorithms used first time in study, both solving extraction problem FDM. The objective function aimed at smallest root mean square error (RMSE) evaluated compared through assessment metrics, computational accuracy, time, statistical methods. minimum RMSE obtained FLA, calculated as 9.8251385E-04 FDM-C 1.6884311E-03 FDM-M. statistically demonstrate reinforce FLA’s success over other algorithms, Friedman test Wilcoxon signed-rank utilized. According tests, FLA produced significantly better results than outperformed them pairwise comparisons. In conclusion, has proven be successful promising extraction, its validated.

Language: Английский

Citations

0