Multijunction solar cell parameter estimation based on metaheuristic algorithms

Parameter estimation in MJSC achieved accurate energy output modeling under diverse environmental conditions. This issue maximizes energy yield and facilitates early fault detection by identifying performance deviations, enabling timely maintenance, and reliable energy production. MJSCs are highly n...

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Main Authors: Marwa M. Elzalabani, Doaa M. Atia, Aref Y. Eliwa, Belal A. Abou Zalam, Mahmoud S. AbouOmar
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Engineering
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Online Access:http://www.sciencedirect.com/science/article/pii/S259012302500372X
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author Marwa M. Elzalabani
Doaa M. Atia
Aref Y. Eliwa
Belal A. Abou Zalam
Mahmoud S. AbouOmar
author_facet Marwa M. Elzalabani
Doaa M. Atia
Aref Y. Eliwa
Belal A. Abou Zalam
Mahmoud S. AbouOmar
author_sort Marwa M. Elzalabani
collection DOAJ
description Parameter estimation in MJSC achieved accurate energy output modeling under diverse environmental conditions. This issue maximizes energy yield and facilitates early fault detection by identifying performance deviations, enabling timely maintenance, and reliable energy production. MJSCs are highly nonlinear and require precise parameter selection. Several mathematical models exist for MJSC parameter estimation— Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM)—each offering a trade-off between complexity and accuracy, improved efficiency, and reliable operation. This paper evaluates three models—SDM, DDM, and TDM—focusing on MJSC parameter estimation. The models include five parameters for SDM, seven for DDM, and nine for TDM, with three additional terms for breakdown voltage alongside conventional parameters. In addition, The Newton-Raphson Method (NRM) hybrid with meta-heuristic algorithms, or MOAs, is used to improve solution estimates, accelerate convergence rates, reduce computational costs, and enhance robustness. The study compared the performance of five optimization algorithms: Grey Wolf Optimizer (GWO), Artificial Hummingbird Algorithm (AHA), Tunicate Swarm Algorithm (TSA), War Strategy Algorithm (WSA), and Moth flam optimizer (MFO). The results demonstrate the superior performance of the AHA algorithm, which has the lowest RMSE value (0.0582) in the SDM and DDM cases, while WSA has the lowest RMSE value (0.0488) in the TDM instance. The MFO algorithm outperforms the GWO algorithm in SDM and DDM scenarios, while the reverse is true for TDM. In terms of errors, and execution time, the TSA algorithm performs the poorest in all three circumstances with RMSE value greater than 0.16.
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spelling doaj-art-f593e97eaaf04a57927613da35353c1d2025-02-12T05:31:44ZengElsevierResults in Engineering2590-12302025-03-0125104287Multijunction solar cell parameter estimation based on metaheuristic algorithmsMarwa M. Elzalabani0Doaa M. Atia1Aref Y. Eliwa2Belal A. Abou Zalam3Mahmoud S. AbouOmar4Photovoltaic Cells Dept, Electronics Research Institute (ERI), Cairo, Egypt; Corresponding author.Photovoltaic Cells Dept, Electronics Research Institute (ERI), Cairo, EgyptPhotovoltaic Cells Dept, Electronics Research Institute (ERI), Cairo, EgyptIndustrial Electronics and Control Engineering Dept, Menofeia University, Menouf, EgyptIndustrial Electronics and Control Engineering Dept, Menofeia University, Menouf, EgyptParameter estimation in MJSC achieved accurate energy output modeling under diverse environmental conditions. This issue maximizes energy yield and facilitates early fault detection by identifying performance deviations, enabling timely maintenance, and reliable energy production. MJSCs are highly nonlinear and require precise parameter selection. Several mathematical models exist for MJSC parameter estimation— Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM)—each offering a trade-off between complexity and accuracy, improved efficiency, and reliable operation. This paper evaluates three models—SDM, DDM, and TDM—focusing on MJSC parameter estimation. The models include five parameters for SDM, seven for DDM, and nine for TDM, with three additional terms for breakdown voltage alongside conventional parameters. In addition, The Newton-Raphson Method (NRM) hybrid with meta-heuristic algorithms, or MOAs, is used to improve solution estimates, accelerate convergence rates, reduce computational costs, and enhance robustness. The study compared the performance of five optimization algorithms: Grey Wolf Optimizer (GWO), Artificial Hummingbird Algorithm (AHA), Tunicate Swarm Algorithm (TSA), War Strategy Algorithm (WSA), and Moth flam optimizer (MFO). The results demonstrate the superior performance of the AHA algorithm, which has the lowest RMSE value (0.0582) in the SDM and DDM cases, while WSA has the lowest RMSE value (0.0488) in the TDM instance. The MFO algorithm outperforms the GWO algorithm in SDM and DDM scenarios, while the reverse is true for TDM. In terms of errors, and execution time, the TSA algorithm performs the poorest in all three circumstances with RMSE value greater than 0.16.http://www.sciencedirect.com/science/article/pii/S259012302500372XMultijunction solar cellMathematical modelMetaheuristic algorithmsParameter estimationNewton RaphsonRMSE
spellingShingle Marwa M. Elzalabani
Doaa M. Atia
Aref Y. Eliwa
Belal A. Abou Zalam
Mahmoud S. AbouOmar
Multijunction solar cell parameter estimation based on metaheuristic algorithms
Results in Engineering
Multijunction solar cell
Mathematical model
Metaheuristic algorithms
Parameter estimation
Newton Raphson
RMSE
title Multijunction solar cell parameter estimation based on metaheuristic algorithms
title_full Multijunction solar cell parameter estimation based on metaheuristic algorithms
title_fullStr Multijunction solar cell parameter estimation based on metaheuristic algorithms
title_full_unstemmed Multijunction solar cell parameter estimation based on metaheuristic algorithms
title_short Multijunction solar cell parameter estimation based on metaheuristic algorithms
title_sort multijunction solar cell parameter estimation based on metaheuristic algorithms
topic Multijunction solar cell
Mathematical model
Metaheuristic algorithms
Parameter estimation
Newton Raphson
RMSE
url http://www.sciencedirect.com/science/article/pii/S259012302500372X
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AT arefyeliwa multijunctionsolarcellparameterestimationbasedonmetaheuristicalgorithms
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