IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10510-d1186378.html
   My bibliography  Save this article

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Author

Listed:
  • Habib Kraiem

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Processes, Energy, Environment and Electrical Systems, National Engineering School of Gabes, University of Gabes, Gabes 6029, Tunisia)

  • Ezzeddine Touti

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Laboratory of Industrial Systems and Renewable Energies, National Higher Engineering School of Tunis, Tunis 1008, Tunisia)

  • Abdulaziz Alanazi

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia)

  • Ahmed M. Agwa

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt)

  • Tarek I. Alanazi

    (Department of Physics, College of Science, Northern Border University, Arar 73222, Saudi Arabia)

  • Mohamed Jamli

    (Laboratory of Industrial Systems and Renewable Energies, National Higher Engineering School of Tunis, Tunis 1008, Tunisia)

  • Lassaad Sbita

    (Processes, Energy, Environment and Electrical Systems, National Engineering School of Gabes, University of Gabes, Gabes 6029, Tunisia)

Abstract

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate.

Suggested Citation

  • Habib Kraiem & Ezzeddine Touti & Abdulaziz Alanazi & Ahmed M. Agwa & Tarek I. Alanazi & Mohamed Jamli & Lassaad Sbita, 2023. "Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10510-:d:1186378
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10510/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/13/10510/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    2. Omnia S. Elazab & Hany M. Hasanien & Ibrahim Alsaidan & Almoataz Y. Abdelaziz & S. M. Muyeen, 2020. "Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm," Energies, MDPI, vol. 13(2), pages 1-15, January.
    3. Stutenbaeumer, Ulrich & Mesfin, Belayneh, 1999. "Equivalent model of monocrystalline, polycrystalline and amorphous silicon solar cells," Renewable Energy, Elsevier, vol. 18(4), pages 501-512.
    4. Manoharan Premkumar & Umashankar Subramaniam & Thanikanti Sudhakar Babu & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "Evaluation of Mathematical Model to Characterize the Performance of Conventional and Hybrid PV Array Topologies under Static and Dynamic Shading Patterns," Energies, MDPI, vol. 13(12), pages 1-37, June.
    5. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad & Nouh, Adnan S., 2019. "Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules," Energy, Elsevier, vol. 187(C).
    6. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    7. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    8. Nader Anani & Haider Ibrahim, 2020. "Adjusting the Single-Diode Model Parameters of a Photovoltaic Module with Irradiance and Temperature," Energies, MDPI, vol. 13(12), pages 1-17, June.
    9. Bastidas-Rodriguez, J.D. & Petrone, G. & Ramos-Paja, C.A. & Spagnuolo, G., 2017. "A genetic algorithm for identifying the single diode model parameters of a photovoltaic panel," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 38-54.
    10. Victoria Kihlström & Jörgen Elbe, 2021. "Constructing Markets for Solar Energy—A Review of Literature about Market Barriers and Government Responses," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    3. Mehmet Yesilbudak, 2021. "Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy," Energies, MDPI, vol. 14(18), pages 1-27, September.
    4. Tong Kang & Jiangang Yao & Min Jin & Shengjie Yang & ThanhLong Duong, 2018. "A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models," Energies, MDPI, vol. 11(5), pages 1-31, April.
    5. 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.
    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).
    7. Li, Shuijia & Gong, Wenyin & Gu, Qiong, 2021. "A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    8. El-Dabah, Mahmoud A. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Saad, Bahaa, 2023. "Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm," Energy, Elsevier, vol. 262(PB).
    9. Arooj Tariq Kiani & Muhammad Faisal Nadeem & Ali Ahmed & Irfan Khan & Rajvikram Madurai Elavarasan & Narottam Das, 2020. "Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization," Energies, MDPI, vol. 13(15), pages 1-26, August.
    10. Houssem Ben Aribia & Ali M. El-Rifaie & Mohamed A. Tolba & Abdullah Shaheen & Ghareeb Moustafa & Fahmi Elsayed & Mostafa Elshahed, 2023. "Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    11. Khan, Firoz & Al-Ahmed, Amir & Al-Sulaiman, Fahad A., 2021. "Critical analysis of the limitations and validity of the assumptions with the analytical methods commonly used to determine the photovoltaic cell parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    12. Peñaranda Chenche, Luz Elena & Hernandez Mendoza, Oscar Saul & Bandarra Filho, Enio Pedone, 2018. "Comparison of four methods for parameter estimation of mono- and multi-junction photovoltaic devices using experimental data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2823-2838.
    13. 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.
    14. 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).
    15. Zhang, Yiying & Ma, Maode & Jin, Zhigang, 2020. "Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models," Energy, Elsevier, vol. 211(C).
    16. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
    17. Jianing Li & Cheng Qin & Chen Yang & Bin Ai & Yecheng Zhou, 2023. "Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an Intelligent Optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function," Energies, MDPI, vol. 16(14), pages 1-23, July.
    18. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    19. Chen, Zhicong & Wu, Lijun & Lin, Peijie & Wu, Yue & Cheng, Shuying, 2016. "Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy," Applied Energy, Elsevier, vol. 182(C), pages 47-57.
    20. Long, Wen & Wu, Tiebin & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2021. "Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm," Energy, Elsevier, vol. 229(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10510-:d:1186378. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.