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Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm

Author

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  • Ebrahimi, S. Mohammadreza
  • Salahshour, Esmaeil
  • Malekzadeh, Milad
  • Francisco Gordillo,

Abstract

The use of solar energy as a source of clean energy is increasing throughout the world. Therefore, designing higher-quality photovoltaic cells has attracted researches. Several equivalent circuits have been proposed for the photovoltaic cell, but it is necessary to note that in order to achieve maximum power point (MPP), finding appropriate circuit model parameters is required. Many methods for finding the optimal parameters have been proposed. In this paper, flexible particle swarm optimization (FPSO) algorithm is proposed to estimate the parameters of PV cell model. In this algorithm, an elimination phase is added to classic PSO. At the beginning of each phase, a certain number of worst particles are deleted and some new particles are replaced in the new search space. Also, the search space of the parameters in each particle is changed based on the value of these parameters. These modifications have enhanced the proposed algorithm performance by adding the ability of global search and also searching in a reasonable space. To highlight the superiority of the FPSO algorithm, this method is used to estimate the parameters of the single diode model, double diode model, and the photovoltaic module. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known optimization methods. Furthermore, to ensure the practical use of the FPSO algorithm, it is validated by three different solar modules such as monocrystalline (SM55) and multi-crystalline (KC200GT) and polycrystalline (SW255). The simulation results show that the proposed algorithm has high performance in terms of accuracy and robustness.

Suggested Citation

  • Ebrahimi, S. Mohammadreza & Salahshour, Esmaeil & Malekzadeh, Milad & Francisco Gordillo,, 2019. "Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm," Energy, Elsevier, vol. 179(C), pages 358-372.
    • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:358-372
    DOI: 10.1016/j.energy.2019.04.218
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    References listed on IDEAS

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    14. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
    15. Hassan Shaban & Essam H. Houssein & Marco Pérez-Cisneros & Diego Oliva & Amir Y. Hassan & Alaa A. K. Ismaeel & Diaa Salama AbdElminaam & Sanchari Deb & Mokhtar Said, 2021. "Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    16. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    17. Adeel, Muhammad & Hassan, Ahmad Kamal & Sher, Hadeed Ahmed & Murtaza, Ali Faisal, 2021. "A grade point average assessment of analytical and numerical methods for parameter extraction of a practical PV device," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    18. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    19. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    20. Ragb, Ola & Bakr, Hanan, 2023. "A new technique for estimation of photovoltaic system and tracking power peaks of PV array under partial shading," Energy, Elsevier, vol. 268(C).
    21. Guojiang Xiong & Jing Zhang & Dongyuan Shi & Xufeng Yuan, 2019. "Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models," Complexity, Hindawi, vol. 2019, pages 1-22, November.
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    23. Samia Charfeddine & Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Víctor Leiva, 2022. "A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems," Mathematics, MDPI, vol. 10(12), pages 1-26, June.

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