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Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Author

Listed:
  • Adel O. Baatiah

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Ali M. Eltamaly

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Majed A. Alotaibi

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

Suggested Citation

  • Adel O. Baatiah & Ali M. Eltamaly & Majed A. Alotaibi, 2023. "Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction," Energies, MDPI, vol. 16(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6433-:d:1233572
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    References listed on IDEAS

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    1. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    2. Ali M. Eltamaly & M. S. Al-Saud & A. G. Abo-Khalil, 2020. "Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
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    4. 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.
    5. Hassan M. H. Farh & Mohd F. Othman & Ali M. Eltamaly & M. S. Al-Saud, 2018. "Maximum Power Extraction from a Partially Shaded PV System Using an Interleaved Boost Converter," Energies, MDPI, vol. 11(10), pages 1-18, September.
    6. Ali M. Eltamaly, 2021. "A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    7. Galal Al-Muthanna & Shuhua Fang & Ibrahim AL-Wesabi & Khaled Ameur & Hossam Kotb & Kareem M. AboRas & Hassan Z. Al Garni & Abdullahi Abubakar Mas’ud, 2023. "A High Speed MPPT Control Utilizing a Hybrid PSO-PID Controller under Partially Shaded Photovoltaic Battery Chargers," Sustainability, MDPI, vol. 15(4), pages 1-28, February.
    8. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    9. Prabha Umapathy & C. Venkataseshaiah & M. Senthil Arumugam, 2010. "Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution," Discrete Dynamics in Nature and Society, Hindawi, vol. 2010, pages 1-15, August.
    10. Chao, Kuei-Hsiang & Lin, Yu-Sheng & Lai, Uei-Dar, 2015. "Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays," Applied Energy, Elsevier, vol. 158(C), pages 609-618.
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    1. Ali M. Eltamaly & Majed A. Alotaibi, 2024. "Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 17(11), pages 1-25, May.

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