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Article

Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem

by
Sultan Hassan Hakmi
1,
Hashim Alnami
1,
Ghareeb Moustafa
1,*,
Ahmed R. Ginidi
2 and
Abdullah M. Shaheen
2,*
1
Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, P.O. Box 114, Jazan 45142, Saudi Arabia
2
Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez P.O. Box 43221, Egypt
*
Authors to whom correspondence should be addressed.
Electronics 2024, 13(9), 1611; https://doi.org/10.3390/electronics13091611
Submission received: 19 March 2024 / Revised: 13 April 2024 / Accepted: 17 April 2024 / Published: 23 April 2024

Abstract

A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize the physical phenomenon of rime-ice growth. It simulates the hard-rime and soft-rime processes, constructing the mechanisms of hard-rime puncture and soft-rime search. In this study, an enhanced version, termed Modified RIME (MRIME), is introduced, integrating a Polynomial Differential Learning Operator (PDLO). The incorporation of PDLO introduces non-linearities to the RIME algorithm, enhancing its adaptability, convergence speed, and global search capability compared to the conventional RIME approach. The proposed MRIME algorithm is designed to identify photovoltaic (PV) module characteristics by considering diverse equivalent circuits, including the One-Diode Model (ONE-DM) and Two-Diode Model TWO-DM, to determine the unspecified parameters of the PV. The MRIME approach is compared to the conventional RIME method using two commercial PV modules, namely the STM6-40/36 module and R.T.C. France cell. The simulation results are juxtaposed with those from contemporary algorithms based on published research. The outcomes related to recent algorithms are also compared with those of the MRIME algorithm in relation to various existing studies. The simulation results indicate that the MRIME algorithm demonstrates substantial improvement rates for the STM6-40/36 module and R.T.C. France cell, achieving 1.16% and 18.45% improvement for the ONE-DM, respectively. For the TWO-DM, it shows significant improvement rates for the two modules, reaching 1.14% and 50.42%, respectively. The MRIME algorithm, in comparison to previously published results, establishes substantial superiority and robustness.
Keywords: RIME optimizer; polynomial differential learning operator; single-diode model; double-diode model; parameter PV cell extraction RIME optimizer; polynomial differential learning operator; single-diode model; double-diode model; parameter PV cell extraction

Share and Cite

MDPI and ACS Style

Hakmi, S.H.; Alnami, H.; Moustafa, G.; Ginidi, A.R.; Shaheen, A.M. Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem. Electronics 2024, 13, 1611. https://doi.org/10.3390/electronics13091611

AMA Style

Hakmi SH, Alnami H, Moustafa G, Ginidi AR, Shaheen AM. Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem. Electronics. 2024; 13(9):1611. https://doi.org/10.3390/electronics13091611

Chicago/Turabian Style

Hakmi, Sultan Hassan, Hashim Alnami, Ghareeb Moustafa, Ahmed R. Ginidi, and Abdullah M. Shaheen. 2024. "Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem" Electronics 13, no. 9: 1611. https://doi.org/10.3390/electronics13091611

APA Style

Hakmi, S. H., Alnami, H., Moustafa, G., Ginidi, A. R., & Shaheen, A. M. (2024). Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem. Electronics, 13(9), 1611. https://doi.org/10.3390/electronics13091611

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