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Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules

Author

Listed:
  • Houssem Ben Aribia

    (Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia
    Electrical Engineering Department, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia)

  • Ali M. El-Rifaie

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Mohamed A. Tolba

    (Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo 11787, Egypt
    Electrical Power Systems Department, National Research University “MPEI”, 111250 Moscow, Russia)

  • Abdullah Shaheen

    (Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ghareeb Moustafa

    (Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia
    Electrical Engineering Department, Suez Canal University, Ismailia 41522, Egypt)

  • Fahmi Elsayed

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Mostafa Elshahed

    (Electrical Engineering Department, Engineering and Information Technology College, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia
    Electrical Power Engineering Department, Faculty of Engineering, Cairo University, Giza 61213, Egypt)

Abstract

One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.

Suggested Citation

  • Houssem Ben Aribia & Ali M. El-Rifaie & Mohamed A. Tolba & Abdullah Shaheen & Ghareeb Moustafa & Fahmi Elsayed & Mostafa Elshahed, 2023. "Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7896-:d:1144830
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    References listed on IDEAS

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    3. Hossam Hassan Ali & Mohamed Ebeed & Ahmed Fathy & Francisco Jurado & Thanikanti Sudhakar Babu & Alaa A. Mahmoud, 2023. "A New Hybrid Multi-Population GTO-BWO Approach for Parameter Estimation of Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(14), pages 1-33, July.
    4. Shufu Yuan & Yuzhang Ji & Yongxu Chen & Xin Liu & Weijun Zhang, 2023. "An Improved Differential Evolution for Parameter Identification of Photovoltaic Models," Sustainability, MDPI, vol. 15(18), pages 1-28, September.

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