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

A reinforcement learning-based ranking teaching-learning-based optimization algorithm for parameters estimation of photovoltaic models DOI
Haoyu Wang, Xiaobing Yu,

Yangchen Lu

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 93, P. 101844 - 101844

Published: Jan. 9, 2025

Language: Английский

Citations

2
Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: part II—a comparative study DOI Creative Commons
Yan Wang, Guojiang Xiong

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(5)

Published: Feb. 14, 2025

Language: Английский

Citations

2
Enhanced Gaining-Sharing knowledge-based algorithm DOI Creative Commons

Mohammed Saeed Jawad,

Heba Sayed Mohamed Roshdy,

Ali Wagdy Mohamed

et al.

Results in Control and Optimization, Journal Year: 2025, Volume and Issue: unknown, P. 100542 - 100542

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
Photovoltaic parameter extraction through an adaptive differential evolution algorithm with multiple linear regression DOI
Bozhen Chen, Haibin Ouyang, Steven Li

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113117 - 113117

Published: April 1, 2025

Language: Английский

Citations

0
Optimal equivalent circuit models for photovoltaic cells and modules using multi-source guided teaching–learning-based optimization DOI Creative Commons

Y.M. Li,

Guojiang Xiong, Seyedali Mirjalili

et al.

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 15(11), P. 102988 - 102988

Published: Aug. 5, 2024

The complexity of equivalent circuit models photovoltaic cells and modules poses a difficult task to the parameter extraction methods. Teaching-learning-based optimization (TLBO) is potent metaheuristic-based method, but it suffers from insufficient precision low dependability. This study presented multi-source guided TLBO through improving its two phases. A approach with one-to-one step-by-step teaching strategies was designed guide different learners in teacher phase. Besides, based on multiple were introduced for knowledge reserves strengthen information exchanging. With improvements, advantageous lessen likelihood hitting local optimum thereby global convergence can be accelerated. resultant method verified single diode model, double three additional modules. findings demonstrate that obtained better solutions dependability, stood out crowd algorithms.

Language: Английский

Citations

3
Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies DOI Open Access
Burçin Özkaya

Black Sea Journal of Engineering and Science, Journal Year: 2024, Volume and Issue: 7(4), P. 771 - 784

Published: July 15, 2024

Recently, solar energy has become an attractive topic for researchers as it been preferred among renewable sources due to its advantages such unlimited supply and low maintenance expenses. The precise modeling of the cells model’s parameter estimate are two most important difficult topics in photovoltaic systems. A cell’s behavior can be predicted based on current-voltage characteristics unknown model parameters. Therefore, many meta-heuristic search algorithms have proposed literature solve PV estimation problem. In this study, enhanced crayfish optimization algorithm (ECOA) with opposition-based learning (OBL) strategies was parameters three different modules. thorough simulation study conducted demonstrate performance ECOA tackling benchmark challenges problems. first using OBL strategies, six variations COA were created. performances these classic tested CEC2020 To determine best variation, results analyzed Friedman Wilcoxon tests. second called ECOA, base applied According results, achieved 1.0880%, 37.8378%, 0.8106% lower error values against STP6-120/36, Photowatt-PWP201, STM6-40/36 Moreover, sensitivity analysis performed order influencing module’s performance. Accordingly, change photo-generated current diode ideality factor single-diode affects modules most. comprehensive showed ECOA’s superior compared other found literature.

Language: Английский

Citations

0
Seasonal short-term photovoltaic power prediction based on GSK–BiGRU–XGboost considering correlation of meteorological factors DOI Creative Commons
Guojiang Xiong, Jing Zhang, Xiaofan Fu

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Nov. 16, 2024

The intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, which greatly affects the reliability supply. To boost prediction accuracy power, a short-term combination model named GSK–BiGRU–XGboost is proposed. First, Pearson correlation coefficient adopted determine highly-correlated meteorological factors construct input features. Second, errors single models are compared, two, i.e., Bidirectional Gated Recurrent Unit (BiGRU) Extreme Gradient Boosting (XGboost) that have smallest lowest selected model. Third, achieve an appropriate weight model, improved gaining sharing knowledge-based algorithm (GSK) based on parameter adaption designed optimize it effectively. Fourth, year-round compared reveal effect characteristics. Finally, influence historical data window with steps investigated. verify performance GSK–BiGRU–XGboost, under weather conditions. achieves high 97.85%, 9.46% 12.43% higher than its member models, respectively. Besides, GSK can lead 1.71% improvement in accuracy.

Language: Английский

Citations

0
Boosting Walrus Optimizer Algorithm based on ranking-based update mechanism for parameters identification of photovoltaic cell models DOI

Taraggy M. Ghanim,

Diaa Salama AbdElminaam, Ayman Nabil

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 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