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PV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm

Author

Listed:
  • Rafa Elshara

    (Department of Material Science and Engineering, University of Kastamonua, Kastamonu 37150, Turkey)

  • Aybaba Hançerlioğullari

    (Department of Physics, University of Kastamonu, Kastamonu 37150, Turkey)

  • Javad Rahebi

    (Department of Software Engineering, Istanbul Topkapi University, Istanbul 34087, Turkey)

  • Jose Manuel Lopez-Guede

    (Department of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

Abstract

In recent times, there have been notable advancements in solar energy and other renewable sources, underscoring their vital contribution to environmental conservation. Solar cells play a crucial role in converting sunlight into electricity, providing a sustainable energy alternative. Despite their significance, effectively optimizing photovoltaic system parameters remains a challenge. To tackle this issue, this study introduces a new optimization approach based on the coati optimization algorithm (COA), which integrates opposition-based learning and chaos theory. Unlike existing methods, the COA aims to maximize power output by integrating solar system parameters efficiently. This strategy represents a significant improvement over traditional algorithms, as evidenced by experimental findings demonstrating improved parameter setting accuracy and a substantial increase in the Friedman rating. As global energy demand continues to rise due to industrial expansion and population growth, the importance of sustainable energy sources becomes increasingly evident. Solar energy, characterized by its renewable nature, presents a promising solution to combat environmental pollution and lessen dependence on fossil fuels. This research emphasizes the critical role of COA-based optimization in advancing solar energy utilization and underscores the necessity for ongoing development in this field.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1716-:d:1369540
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    References listed on IDEAS

    as
    1. 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.
    2. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    3. Hasanien, Hany M. & Alsaleh, Ibrahim & Alassaf, Abdullah & Alateeq, Ayoob, 2023. "Enhanced coati optimization algorithm-based optimal power flow including renewable energy uncertainties and electric vehicles," Energy, Elsevier, vol. 283(C).
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