A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models

This study addresses the challenges in accurately estimating photovoltaic (PV) parameters for solar energy applications by enhancing parameter extraction processes to improve the efficiency of PV models. An information gap in PV solar cell and module parameters provided by vendors obstructs accurate...

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Vydáno v:	Electrical engineering Ročník 106; číslo 5; s. 6565 - 6585
Hlavní autoři:	Izci, Davut, Ekinci, Serdar, Altalhi, Maryam, Daoud, Mohammad Sh, Migdady, Hazem, Abualigah, Laith
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024 Springer Nature B.V
Témata:	Accuracy Alternative energy sources Diodes Economics and Management Electrical Engineering Electrical Machines and Networks Energy Policy Engineering Heuristic methods Mountains Optimization Optimization algorithms Optimization techniques Original Paper Parameter estimation Parameter identification Parameter modification Pattern search Photovoltaic cells Power Electronics Search algorithms Silicon Solar cells Solar energy France Pattern search algorithm Parameter estimation Renewable energy Solar cell models Mountain gazelle optimizer
ISSN:	0948-7921, 1432-0487
On-line přístup:	Získat plný text
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Abstract	This study addresses the challenges in accurately estimating photovoltaic (PV) parameters for solar energy applications by enhancing parameter extraction processes to improve the efficiency of PV models. An information gap in PV solar cell and module parameters provided by vendors obstructs accurate simulation. Traditional numerical techniques face limitations in accurately solving complex nonlinear optimization problems. As a solution, metaheuristic algorithms, specifically the mountain gazelle optimizer, are proposed. To overcome limitations of the mountain gazelle optimizer, a pattern search algorithm is integrated for a more robust global and local search. Rigorous testing demonstrates superior performance in achieving lower best values and tighter standard deviations compared to existing algorithms, making it a promising and efficient optimizer for accurate parameter estimation in various solar cell and module models, including the R.T.C. France silicon solar cell and Photowatt-PWP201 PV module. The proposed optimizer excels in estimating parameters for both single diode, double diode, and PV module models, outperforming state-of-the-art algorithms and showcasing its potential for reliable and precise optimization in solar cell modeling applications.
AbstractList	This study addresses the challenges in accurately estimating photovoltaic (PV) parameters for solar energy applications by enhancing parameter extraction processes to improve the efficiency of PV models. An information gap in PV solar cell and module parameters provided by vendors obstructs accurate simulation. Traditional numerical techniques face limitations in accurately solving complex nonlinear optimization problems. As a solution, metaheuristic algorithms, specifically the mountain gazelle optimizer, are proposed. To overcome limitations of the mountain gazelle optimizer, a pattern search algorithm is integrated for a more robust global and local search. Rigorous testing demonstrates superior performance in achieving lower best values and tighter standard deviations compared to existing algorithms, making it a promising and efficient optimizer for accurate parameter estimation in various solar cell and module models, including the R.T.C. France silicon solar cell and Photowatt-PWP201 PV module. The proposed optimizer excels in estimating parameters for both single diode, double diode, and PV module models, outperforming state-of-the-art algorithms and showcasing its potential for reliable and precise optimization in solar cell modeling applications.
Author	Abualigah, Laith Migdady, Hazem Ekinci, Serdar Altalhi, Maryam Izci, Davut Daoud, Mohammad Sh
Author_xml	– sequence: 1 givenname: Davut surname: Izci fullname: Izci, Davut organization: Department of Computer Engineering, Batman University, Applied science research center, Applied science private university – sequence: 2 givenname: Serdar surname: Ekinci fullname: Ekinci, Serdar organization: Department of Computer Engineering, Batman University – sequence: 3 givenname: Maryam surname: Altalhi fullname: Altalhi, Maryam organization: Department of Management Information Systems, College of Business Administration, Taif University – sequence: 4 givenname: Mohammad Sh surname: Daoud fullname: Daoud, Mohammad Sh organization: College of Engineering, Al Ain University – sequence: 5 givenname: Hazem surname: Migdady fullname: Migdady, Hazem organization: CSMIS Department, Oman College of Management and Technology – sequence: 6 givenname: Laith surname: Abualigah fullname: Abualigah, Laith email: aligah.2020@gmail.com organization: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Computer Science Department, Al Al-Bayt University, Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, MEU Research Unit, Middle East University, Department of Electrical and Computer Engineering, Lebanese American University, School of Engineering and Technology, Sunway University Malaysia
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Snippet	This study addresses the challenges in accurately estimating photovoltaic (PV) parameters for solar energy applications by enhancing parameter extraction...
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SubjectTerms	Accuracy Alternative energy sources Diodes Economics and Management Electrical Engineering Electrical Machines and Networks Energy Policy Engineering Heuristic methods Mountains Optimization Optimization algorithms Optimization techniques Original Paper Parameter estimation Parameter identification Parameter modification Pattern search Photovoltaic cells Power Electronics Search algorithms Silicon Solar cells Solar energy
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Title	A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models
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