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A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems

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  • Ali M. Eltamaly

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
    Department of Electrical Engineering, Mansoura University, Mansoura 35516, Egypt
    K.A. CARE Energy Research and Innovation Center, Riyadh 11451, Saudi Arabia)

Abstract

This study introduces a novel strategy that can determine the optimal values of control parameters of a PSO. These optimal control parameters will be very valuable to all the online optimization problems where the convergence time and the failure convergence rate are vital concerns. The newly proposed strategy uses two nested PSO (NESTPSO) searching loops; the inner one contained the original objective function, and the outer one used the inner PSO as a fitness function. The control parameters and the swarm size acted as the optimization variables for the outer loop. These variables were optimized for the lowest premature convergence rate, the lowest number of iterations, and the lowest swarm size. The new proposed strategy can be used for all the swarm optimization techniques as well. The results showed the superiority of the proposed NESTPSO control parameter determination when compared with several state of the art PSO strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:1008-:d:483312
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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. 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.
    3. S. Geetha & G. Poonthalir & P.T. Vanathi, 2013. "Nested particle swarm optimisation for multi-depot vehicle routing problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 16(3), pages 329-348.
    4. 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.
    5. Frank A. Tillman, 1969. "The Multiple Terminal Delivery Problem with Probabilistic Demands," Transportation Science, INFORMS, vol. 3(3), pages 192-204, August.
    6. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
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    Citations

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    Cited by:

    1. Majed A. Alotaibi & Ali M. Eltamaly, 2021. "A Smart Strategy for Sizing of Hybrid Renewable Energy System to Supply Remote Loads in Saudi Arabia," Energies, MDPI, vol. 14(21), pages 1-24, October.
    2. Phiraphat Antarasee & Suttichai Premrudeepreechacharn & Apirat Siritaratiwat & Sirote Khunkitti, 2022. "Optimal Design of Electric Vehicle Fast-Charging Station’s Structure Using Metaheuristic Algorithms," Sustainability, MDPI, vol. 15(1), pages 1-22, December.
    3. Liu, Yongjie & Huang, Zhiwu & He, Liang & Pan, Jianping & Li, Heng & Peng, Jun, 2023. "Temperature-aware charging strategy for lithium-ion batteries with adaptive current sequences in cold environments," Applied Energy, Elsevier, vol. 352(C).
    4. Askhat Diveev & Elizaveta Shmalko, 2023. "Adaptive Synthesized Control for Solving the Optimal Control Problem," Mathematics, MDPI, vol. 11(19), pages 1-18, September.
    5. Ekene Gabriel Okafor & Whit Vinson & David Ryan Huitink, 2023. "Effect of Stress Interaction on Multi-Stress Accelerated Life Test Plan: Assessment Based on Particle Swarm Optimization," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    6. Muhannad Alaraj & Astitva Kumar & Ibrahim Alsaidan & Mohammad Rizwan & Majid Jamil, 2022. "An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    7. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.
    8. Marcus Evandro Teixeira Souza Junior & Luiz Carlos Gomes Freitas, 2022. "Power Electronics for Modern Sustainable Power Systems: Distributed Generation, Microgrids and Smart Grids—A Review," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    9. 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.
    10. Eltamaly, Ali M., 2021. "A novel musical chairs algorithm applied for MPPT of PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    11. Ali M. Eltamaly, 2021. "An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-26, February.

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