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An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models

Author

Listed:
  • Mohamed Abdel-Basset

    (Department of Computer Science, Zagazig University, Shaibet an Nakareyah, Zagazig 44519, Egypt)

  • Reda Mohamed

    (Department of Computer Science, Zagazig University, Shaibet an Nakareyah, Zagazig 44519, Egypt)

  • Ripon K. Chakrabortty

    (Capability Systems Centre, School of Engineering and IT, UNSW Canberra 2052, Australia)

  • Michael J. Ryan

    (Capability Systems Centre, School of Engineering and IT, UNSW Canberra 2052, Australia)

  • Attia El-Fergany

    (Department of Electric Power & Machines, Zagazig University, Shaibet an Nakareyah, Zagazig 44519, Egypt)

Abstract

The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.

Suggested Citation

  • Mohamed Abdel-Basset & Reda Mohamed & Ripon K. Chakrabortty & Michael J. Ryan & Attia El-Fergany, 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models," Energies, MDPI, vol. 14(7), pages 1-33, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1867-:d:525428
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    References listed on IDEAS

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    Cited by:

    1. Gang Hu & Jiao Wang & Min Li & Abdelazim G. Hussien & Muhammad Abbas, 2023. "EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications," Mathematics, MDPI, vol. 11(4), pages 1-32, February.
    2. Abdullah Shaheen & Ragab El-Sehiemy & Salah Kamel & Ali Selim, 2022. "Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm," Energies, MDPI, vol. 15(19), pages 1-14, September.
    3. Rafa Elshara & Aybaba Hançerlioğullari & Javad Rahebi & Jose Manuel Lopez-Guede, 2024. "PV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm," Energies, MDPI, vol. 17(7), pages 1-26, April.
    4. Husham Muayad Nayyef & Ahmad Asrul Ibrahim & Muhammad Ammirrul Atiqi Mohd Zainuri & Mohd Asyraf Zulkifley & Hussain Shareef, 2023. "A Novel Hybrid Algorithm Based on Jellyfish Search and Particle Swarm Optimization," Mathematics, MDPI, vol. 11(14), pages 1-29, July.
    5. Rizk-Allah, Rizk M. & El-Fergany, Attia A., 2021. "Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model," Energy, Elsevier, vol. 237(C).
    6. Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    7. Afroz Alam & Preeti Verma & Mohd Tariq & Adil Sarwar & Basem Alamri & Noore Zahra & Shabana Urooj, 2021. "Jellyfish Search Optimization Algorithm for MPP Tracking of PV System," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    8. Mohamed Abdel-Basset & Reda Mohamed & Attia El-Fergany & Sameh S. Askar & Mohamed Abouhawwash, 2021. "Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations," Energies, MDPI, vol. 14(13), pages 1-20, June.

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