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On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions

Author

Listed:
  • Wafa Hayder

    (Société de Construction et d’Équipement, Gabes 6001, Tunisia)

  • Dezso Sera

    (Faculty of Science and Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia)

  • Emanuele Ogliari

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

  • Abderezak Lashab

    (Department of Energy Technology, Center for Research on Microgrids (CROM), Aalborg University, Pontoppidanstraede 111, DK-9220 Aalborg, Denmark)

Abstract

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7668-:d:945230
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    References listed on IDEAS

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    1. 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.
    2. Belkaid, A. & Colak, I. & Isik, O., 2016. "Photovoltaic maximum power point tracking under fast varying of solar radiation," Applied Energy, Elsevier, vol. 179(C), pages 523-530.
    3. Mohammadmehdi Seyedmahmoudian & Saad Mekhilef & Rasoul Rahmani & Rubiyah Yusof & Ehsan Taslimi Renani, 2013. "Analytical Modeling of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 6(1), pages 1-17, January.
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    Cited by:

    1. Ashwin Kumar Devarakonda & Natarajan Karuppiah & Tamilselvi Selvaraj & Praveen Kumar Balachandran & Ravivarman Shanmugasundaram & Tomonobu Senjyu, 2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems," Energies, MDPI, vol. 15(22), pages 1-30, November.
    2. Miao Zhang & Keyu Zhuang & Tong Zhao & Jingze Xue & Yunlong Gao & Shuai Cui & Zheng Qiao, 2022. "MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control," Energies, MDPI, vol. 15(23), pages 1-19, 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.

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