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An Efficient Electric Charged Particles Optimization Algorithm for Numerical Optimization and Optimal Estimation of Photovoltaic Models

Author

Listed:
  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Essam H. Houssein

    (Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Mohamed H. Hassan

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mokhtar Shouran

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Fatma A. Hashim

    (Faculty of Engineering, Helwan University, Cairo 11795, Egypt)

Abstract

The electric charged particles optimization (ECPO) technique is inspired by the interaction (exerted forces) between electrically charged particles. A developed version of ECPO called MECPO is suggested in this article to enhance the capability of searching and balancing the exploitation and exploration phases of the conventional ECPO. To let the search agent jumps out from the local optimum and avoid stagnation in the local optimum in the proposed MECPO, three different strategies in the interaction between ECPs are modified in conjunction with the conventional ECPO. Therefore, the convergence rate is enhanced and reaches rapidly to the optimal solution. To evaluate the effectiveness of the MECPO, it is executed on the test functions of the CEC’17. Furthermore, the MECPO technique is suggested to estimate the parameters of different photovoltaic models, such as the single-diode model (SDM), the double-diode model (DDM), and the triple-diode model (TDM). The simulation results illustrate the validation and effectiveness of MECPO in extracting parameters from photovoltaic models.

Suggested Citation

  • Salah Kamel & Essam H. Houssein & Mohamed H. Hassan & Mokhtar Shouran & Fatma A. Hashim, 2022. "An Efficient Electric Charged Particles Optimization Algorithm for Numerical Optimization and Optimal Estimation of Photovoltaic Models," Mathematics, MDPI, vol. 10(6), pages 1-34, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:913-:d:770107
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    References listed on IDEAS

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    Cited by:

    1. Mohamed H. Hassan & Salah Kamel & José Luís Domínguez-García & Mohamed F. El-Naggar, 2022. "MSSA-DEED: A Multi-Objective Salp Swarm Algorithm for Solving Dynamic Economic Emission Dispatch Problems," Sustainability, MDPI, vol. 14(15), pages 1-23, August.

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