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A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

Author

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  • Diego Oliva

    (Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, 44430 Jalisco, Mexico
    Institute of Cybernetics, Tomsk Polytechnic University, 634050 Tomsk, Russia
    Scientific Research Group in Egypt (SRGE), Cairo 12613, Egypt)

  • Ahmed A. Ewees

    (Scientific Research Group in Egypt (SRGE), Cairo 12613, Egypt
    Department of Computer, Damietta University, Damietta 34517, Egypt)

  • Mohamed Abd El Aziz

    (Scientific Research Group in Egypt (SRGE), Cairo 12613, Egypt
    School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Aboul Ella Hassanien

    (Scientific Research Group in Egypt (SRGE), Cairo 12613, Egypt
    Faculty of Computers Information, Cairo University, Cairo 12637, Egypt)

  • Marco Peréz-Cisneros

    (Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, 44430 Jalisco, Mexico)

Abstract

The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun’s radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.

Suggested Citation

  • Diego Oliva & Ahmed A. Ewees & Mohamed Abd El Aziz & Aboul Ella Hassanien & Marco Peréz-Cisneros, 2017. "A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells," Energies, MDPI, vol. 10(7), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:865-:d:102883
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    References listed on IDEAS

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    4. Chin, Vun Jack & Salam, Zainal, 2019. "A New Three-point-based Approach for the Parameter Extraction of Photovoltaic Cells," Applied Energy, Elsevier, vol. 237(C), pages 519-533.
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    6. Lin, Xiankun & Wu, Yuhang, 2020. "Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture," Energy, Elsevier, vol. 196(C).
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    8. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    9. Reem Y. Abdelghany & Salah Kamel & Hamdy M. Sultan & Ahmed Khorasy & Salah K. Elsayed & Mahrous Ahmed, 2021. "Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation," Sustainability, MDPI, vol. 13(7), pages 1-22, March.
    10. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang, 2022. "A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model," Energies, MDPI, vol. 15(4), pages 1-16, February.
    11. Shufu Yuan & Yuzhang Ji & Yongxu Chen & Xin Liu & Weijun Zhang, 2023. "An Improved Differential Evolution for Parameter Identification of Photovoltaic Models," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
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    13. Fathy, Ahmed & Elaziz, Mohamed Abd & Sayed, Enas Taha & Olabi, A.G. & Rezk, Hegazy, 2019. "Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm," Energy, Elsevier, vol. 188(C).
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