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Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm

Author

Listed:
  • Mohamed Abdel-Basset

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Reda Mohamed

    (Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Attia El-Fergany

    (Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Mohamed Abouhawwash

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
    Department of Computational Mathematics, Science, and Engineering (CMSE), College of Engineering, Micigan State University, East Lansing, MI 48824, USA)

  • S. S. Askar

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
    Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.

Suggested Citation

  • Mohamed Abdel-Basset & Reda Mohamed & Attia El-Fergany & Mohamed Abouhawwash & S. S. Askar, 2021. "Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm," Mathematics, MDPI, vol. 9(9), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:995-:d:545122
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    References listed on IDEAS

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    Cited by:

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    2. Ragb, Ola & Bakr, Hanan, 2023. "A new technique for estimation of photovoltaic system and tracking power peaks of PV array under partial shading," Energy, Elsevier, vol. 268(C).
    3. Rizk-Allah, Rizk M. & El-Fergany, Attia A., 2021. "Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model," Energy, Elsevier, vol. 237(C).
    4. Ridha, Hussein Mohammed & Hizam, Hashim & Mirjalili, Seyedali & Othman, Mohammad Lutfi & Ya'acob, Mohammad Effendy & Ahmadipour, Masoud, 2022. "Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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