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A Study of a Generalized Photovoltaic System with MPPT Using Perturb and Observer Algorithms under Varying Conditions

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  • Zulfiqar Ali

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
    These authors contributed equally to this work.)

  • Syed Zagam Abbas

    (Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250, Pakistan
    These authors contributed equally to this work.)

  • Anzar Mahmood

    (Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250, Pakistan)

  • Syed Wajahat Ali

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan)

  • Syed Bilal Javed

    (Department Electrical Engineering, COMSATS University, Islamabad 45550, Pakistan)

  • Chun-Lien Su

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan)

Abstract

In recent years, renewable energy (RE) has shown promise as a sustainable solution to the rising energy demand worldwide. Photovoltaic (PV) technology has emerged as a highly viable RE alternative. The majority of PV schemes use specific PV models with specified parameters. This study proposes a PV model with generic specifications, a PV array, a DC/DC converter, a DC/AC inverter, maximum power point tracking (MPPT), and grid synchronization using a feedback control system under the MATLAB/Simulink environment. Various MPPT techniques have been adapted to track the PV’s maximum power point (MPP); however, there are various uncertainties. To address these challenges, this paper presented a perturb and observe (P&O) strategy to track the MPP of PV systems reliably. The MPP of a PV system varies according to meteorological order, such as solar radiation and cell temperature. The MPPT primarily gathers the maximum current and voltage of the PV array and provides them to the load using a boost converter. The MPPT performance and PV array attributes are analyzed during abrupt weather changes. Finally, a feedback controller is configured to perform synchronization of the inverter with the grid. The validity and reliability of the PV module using P&O methods provide a higher efficacy of MPPT under MATLAB/simulation. Finally, the presented results endorse the strength of the proposed technique.

Suggested Citation

  • Zulfiqar Ali & Syed Zagam Abbas & Anzar Mahmood & Syed Wajahat Ali & Syed Bilal Javed & Chun-Lien Su, 2023. "A Study of a Generalized Photovoltaic System with MPPT Using Perturb and Observer Algorithms under Varying Conditions," Energies, MDPI, vol. 16(9), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3638-:d:1131031
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    References listed on IDEAS

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    1. Syed Zagam Abbas & Zulfiqar Ali & Anzar Mahmood & Syed Quosain Haider & Anila Kousar & Sohail Razzaq & Tehzeeb Ul Hassan & Chun-Lien Su, 2022. "Review of Smart Grid and Nascent Energy Policies: Pakistan as a Case Study," Energies, MDPI, vol. 15(19), pages 1-23, September.
    2. Das, Narottam & Wongsodihardjo, Hendy & Islam, Syed, 2015. "Modeling of multi-junction photovoltaic cell using MATLAB/Simulink to improve the conversion efficiency," Renewable Energy, Elsevier, vol. 74(C), pages 917-924.
    3. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. Eldin, S.A. Sharaf & Abd-Elhady, M.S. & Kandil, H.A., 2016. "Feasibility of solar tracking systems for PV panels in hot and cold regions," Renewable Energy, Elsevier, vol. 85(C), pages 228-233.
    5. Musong L. Katche & Augustine B. Makokha & Siagi O. Zachary & Muyiwa S. Adaramola, 2023. "A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems," Energies, MDPI, vol. 16(5), pages 1-23, February.
    6. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    7. Wafa Hayder & Emanuele Ogliari & Alberto Dolara & Aycha Abid & Mouna Ben Hamed & Lasaad Sbita, 2020. "Improved PSO: A Comparative Study in MPPT Algorithm for PV System Control under Partial Shading Conditions," Energies, MDPI, vol. 13(8), pages 1-22, April.
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    3. Yu Deng & Jingang Han, 2024. "Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control," Energies, MDPI, vol. 17(14), pages 1-26, July.

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