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

Accurate parameters extraction of photovoltaic models using Lambert W-function collaborated with AI-based Puma optimization method DOI Creative Commons
Rabeh Abbassi, Salem Saidi, Houssem Jerbi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104268 - 104268

Published: Feb. 1, 2025

Language: Английский

Citations

0
Efficient identification of photovoltaic cell parameters via Bayesian Neural Network-artificial ecosystem optimization algorithm DOI Creative Commons
Bo Yang,

Ruyi Zheng,

Yucun Qian

et al.

Global Energy Interconnection, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Comparative Analysis of Rate of Penetration Prediction and Optimization in Deep Wells Using Real-Time Continuous Stacked Generalization Ensemble Learning: A Case Study in Shunbei Field DOI

Omer Ahmednour,

Chen Dong,

J.W.S. Liu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Modulation optimization method for seven-level SHEPWM inverter based on EPSO algorithm DOI Creative Commons

Renzheng Wang,

Yuncheng Zhang, Huiling Chen

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 30, 2024

Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) has excellent harmonic characteristics, but its nonlinear transcendental system of equations is difficult to be solved, and the practical application encounters a bottleneck. In this paper, modulation optimization method for seven-level SHEPWM inverter based on Evolutionary Particle Swarm Optimization (EPSO) algorithm proposed address problem, so that quickly converges global optimum solution. The EPSO incorporates population strategy in two phases improve diversity real time. initialization phase, initialized optimized using Opposition-Based Learning (OBL) quality initial population. iterative stage, we combine adaptive (PSO) algorithm, Tunicate Algorithm (TSA), Adaptive Gaussian Variation, Quasi-Opposition-Based (QOBL) other methods solve problem insufficient process searching optimal solution, break through local optimum, convergence speed accuracy algorithm. Experiments 19 benchmark functions show ability ahead TSA, INFO, MA (Mayfly Algorithm), EO (Equilibrium Optimizer) algorithms. solution about three times PSO, which achieves fast highly accurate convergence, with small error output inverter, better distortion rate than standard requirement.

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