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Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer

Author

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  • Abd-ElHady Ramadan

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Tahir Khurshaid

    (Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Korea)

  • Seung-Ryle Oh

    (Korea Electric Power Company (KEPCO), Deajon 24056, Korea)

  • Sang-Bong Rhee

    (Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters estimation using optimization algorithms is a challenging problem in which a wide range of research has been conducted. The idea behind this challenge is the selection of a proper PV model and algorithm to estimate the accurate parameters of this model. In this paper, a new application of the improved gray wolf optimizer (I-GWO) is proposed to estimate the parameters’ values that achieve an accurate PV three diode model (TDM) in a perfect and robust manner. The PV TDM is developed to represent the effect of grain boundaries and large leakage current in the PV system. I-GWO is developed with the aim of improving population, exploration and exploitation balance and convergence of the original GWO. The performance of I-GWO is compared with other well-known optimization algorithms. I-GWO is evaluated through two different applications. In the first application, the real data from RTC furnace is applied and in the second one, the real data of PTW polycrystalline PV panel is applied. The results are compared with different evaluation factors (root mean square error (RMSE), current absolute error and statistical analysis for multiple independent runs). I-GWO achieved the lowest RMSE values in comparison with other algorithms. The RMSE values for the two applications are 0.00098331 and 0.0024276, respectively. Based on quantitative and qualitative performance evaluation, it can be concluded that the estimated parameters of TDM by I-GWO are more accurate than those obtained by other studied optimization algorithms.

Suggested Citation

  • Abd-ElHady Ramadan & Salah Kamel & Tahir Khurshaid & Seung-Ryle Oh & Sang-Bong Rhee, 2021. "Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6963-:d:578942
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    2. Isaac Amoussou & Emmanuel Tanyi & Lajmi Fatma & Takele Ferede Agajie & Ilyes Boulkaibet & Nadhira Khezami & Ahmed Ali & Baseem Khan, 2023. "The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant," Sustainability, MDPI, vol. 15(15), pages 1-29, July.
    3. Mostafa Elshahed & Ali M. El-Rifaie & Mohamed A. Tolba & Ahmed Ginidi & Abdullah Shaheen & Shazly A. Mohamed, 2022. "An Innovative Hunter-Prey-Based Optimization for Electrically Based Single-, Double-, and Triple-Diode Models of Solar Photovoltaic Systems," Mathematics, MDPI, vol. 10(23), pages 1-22, December.
    4. Abdelhady Ramadan & Salah Kamel & I. Hamdan & Ahmed M. Agwa, 2022. "A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
    5. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.

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