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An Immune Firefly Algorithm for Tracking the Maximum Power Point of PV Array under Partial Shading Conditions

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  • Mingrui Zhang

    (Department of Electrical Engineering, Tongji University, Shanghai 201804, China)

  • Zheyang Chen

    (Department of Electrical Engineering, Tongji University, Shanghai 201804, China)

  • Li Wei

    (Department of Electrical Engineering, Tongji University, Shanghai 201804, China)

Abstract

Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.

Suggested Citation

  • Mingrui Zhang & Zheyang Chen & Li Wei, 2019. "An Immune Firefly Algorithm for Tracking the Maximum Power Point of PV Array under Partial Shading Conditions," Energies, MDPI, vol. 12(16), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3083-:d:256461
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    References listed on IDEAS

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    1. 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. Nouman Akram & Laiq Khan & Shahrukh Agha & Kamran Hafeez, 2022. "Global Maximum Power Point Tracking of Partially Shaded PV System Using Advanced Optimization Techniques," Energies, MDPI, vol. 15(11), pages 1-29, May.
    2. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    3. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
    4. Hsen Abidi & Lilia Sidhom & Ines Chihi, 2023. "Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques," Energies, MDPI, vol. 16(8), pages 1-45, April.
    5. Novie Ayub Windarko & Muhammad Nizar Habibi & Bambang Sumantri & Eka Prasetyono & Moh. Zaenal Efendi & Taufik, 2021. "A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions," Energies, MDPI, vol. 14(2), pages 1-22, January.

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