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Improved PSO: A Comparative Study in MPPT Algorithm for PV System Control under Partial Shading Conditions

Author

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  • Wafa Hayder

    (Department of Electrical Engineering, National Engineering School of Gabes, 6029 Gabes, Tunisia)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Alberto Dolara

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Aycha Abid

    (Department of Electrical Engineering, National Engineering School of Gabes, 6029 Gabes, Tunisia)

  • Mouna Ben Hamed

    (Department of Electrical Engineering, National Engineering School of Gabes, 6029 Gabes, Tunisia)

  • Lasaad Sbita

    (Department of Electrical Engineering, National Engineering School of Gabes, 6029 Gabes, Tunisia)

Abstract

This paper deals with the implementation and analysis of a new maximum power point tracking (MPPT) control method, which is tested under variable climatic conditions. This new MPPT strategy has been created for photovoltaic systems based on Particle Swarm Optimization (PSO). The novel Improved Particle Swarm Optimization (IPSO) algorithm is tested in several simulations which have been implemented in view of the various system responses such as: voltage, current, and power. The performances of the proposed IPSO algorithm have been completed and compared with results of well-established methods adopted in the literature showing a higher accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2035-:d:347639
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    References listed on IDEAS

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    1. Ishaque, Kashif & Salam, Zainal, 2013. "A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 475-488.
    2. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    3. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
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    Citations

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    Cited by:

    1. Alessandro Niccolai & Alberto Dolara & Emanuele Ogliari, 2021. "Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches," Energies, MDPI, vol. 14(2), pages 1-18, January.
    2. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    3. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.
    4. Wafa Hayder & Dezso Sera & Emanuele Ogliari & Abderezak Lashab, 2022. "On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions," Energies, MDPI, vol. 15(20), pages 1-15, October.
    5. 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.

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