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

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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.

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Data are available from the authors upon reasonable request.

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Acknowledgements

The researchers would like to acknowledge Deanship of Scientific Research, Taif University for funding this work

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Not Applicable.

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Authors and Affiliations

  1. Department of Computer Engineering, Batman University, 72100, Batman, Turkey

    Davut Izci & Serdar Ekinci

  2. Applied science research center, Applied science private university, Amman, 11931, Jordan

    Davut Izci

  3. Department of Management Information Systems, College of Business Administration, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia

    Maryam Altalhi

  4. College of Engineering, Al Ain University, 112612, Abu Dhabi, United Arab Emirates

    Mohammad Sh. Daoud

  5. CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman

    Hazem Migdady

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  7. Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  8. Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia

    Laith Abualigah

  9. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  10. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  11. School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia

    Laith Abualigah

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  1. Davut Izci
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Contributions

Davut Izci was contributed to software, resources, writing—original draft, supervision, methodology, conceptualization, formal analysis, review and editing. Serdar Ekinci was contributed to supervision, methodology, conceptualization, writing—original draft. Maryam Altalhi was contributed to formal analysis, writing—review and editing. Mohammad Sh. Daoud was contributed to formal analysis, writing—review and editing. Hazem Migdady was contributed to formal analysis, writing—review and editing. Laith Abualigah was contributed to formal analysis, writing—review and editing. All authors read and approved the final paper.

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Correspondence to Laith Abualigah.

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Izci, D., Ekinci, S., Altalhi, M. et al. A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02375-y

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