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Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity

Author

Listed:
  • Eduardo Manuel Godinho Rodrigues

    (Management and Production Technologies of Northern Aveiro—ESAN, Estrada do Cercal, 449, Santiago de Riba-Ul, 3720-509 Oliveira de Azeméis, Portugal)

  • Radu Godina

    (C-MAST—Centre for Aerospace Science and Technologies—Department of Electromechanical Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • Mousa Marzband

    (Faculty of Engineering and Environment, Department of Physics and Electrical Engineering, Northumbria University Newcastle, Newcastle upon Tyne NE18ST, UK)

  • Edris Pouresmaeil

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

Abstract
The amount of energy generated from a photovoltaic installation depends mainly on two factors—the temperature and solar irradiance. Numerous maximum power point tracking (MPPT) techniques have been developed for photovoltaic systems. The challenge is what method to employ in order to obtain optimum operating points (voltage and current) automatically at the maximum photovoltaic output power in most conditions. This paper is focused on the structural analysis of mathematical models of PV cells with growing levels of complexity. The main objective is to simulate and compare the characteristic current-voltage (I-V) and power-voltage (P-V) curves of equivalent circuits of the ideal PV cell model and, with one and with two diodes, that is, equivalent circuits with five and seven parameters. The contribution of each parameter is analyzed in the particular context of a given model and then generalized through comparison to a more complex model. In this study the numerical simulation of the models is used intensively and extensively. The approach utilized to model the equivalent circuits permits an adequate simulation of the photovoltaic array systems by considering the compromise between the complexity and accuracy. By utilizing the Newton–Raphson method the studied models are then employed through the use of Matlab/Simulink. Finally, this study concludes with an analysis and comparison of the evolution of maximum power observed in the models.

Suggested Citation

  • Eduardo Manuel Godinho Rodrigues & Radu Godina & Mousa Marzband & Edris Pouresmaeil, 2018. "Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity," Energies, MDPI, vol. 11(11), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2902-:d:178242
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    References listed on IDEAS

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    2. Azaioud, Hakim & Farnam, Arash & Knockaert, Jos & Vandevelde, Lieven & Desmet, Jan, 2024. "Efficiency optimisation and converterless PV integration by applying a dynamic voltage on an LVDC backbone," Applied Energy, Elsevier, vol. 356(C).
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    4. Abdelali El Aroudi & Mohamed Al-Numay & Germain Garcia & Khalifa Al Hossani & Naji Al Sayari & Angel Cid-Pastor, 2018. "Analysis of Nonlinear Dynamics of a Quadratic Boost Converter Used for Maximum Power Point Tracking in a Grid-Interlinked PV System," Energies, MDPI, vol. 12(1), pages 1-23, December.
    5. Germán Herrera Vidal & Jairo R. Coronado-Hernández & Claudia Minnaard, 2023. "Measuring manufacturing system complexity: a literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2865-2888, October.
    6. Arzhang Yousefi-Talouki & Shaghayegh Zalzar & Edris Pouresmaeil, 2019. "Direct Power Control of Matrix Converter-Fed DFIG with Fixed Switching Frequency," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    7. Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    8. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    9. Thiago B. Murari & Aloisio S. Nascimento Filho & Marcelo A. Moret & Sergio Pitombo & Alex A. B. Santos, 2020. "Self-Affine Analysis of ENSO in Solar Radiation," Energies, MDPI, vol. 13(18), pages 1-17, September.
    10. Arsalan Najafi & Mousa Marzband & Behnam Mohamadi-Ivatloo & Javier Contreras & Mahdi Pourakbari-Kasmaei & Matti Lehtonen & Radu Godina, 2019. "Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response," Energies, MDPI, vol. 12(8), pages 1-20, April.
    11. Ehsan Norouzzadeh & Ahmad Ale Ahmad & Meysam Saeedian & Gholamreza Eini & Edris Pouresmaeil, 2019. "Design and Implementation of a New Algorithm for Enhancing MPPT Performance in Solar Cells," Energies, MDPI, vol. 12(3), pages 1-17, February.

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