IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5425-d1195877.html
   My bibliography  Save this article

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

Author

Listed:
  • Jianing Li

    (School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China)

  • Cheng Qin

    (School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China)

  • Chen Yang

    (School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China)

  • Bin Ai

    (School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
    Guangdong Provincial Key Laboratory of Photovoltaic Technologies, Sun Yat-Sen University, Guangzhou 510006, China)

  • Yecheng Zhou

    (School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China)

Abstract

In this paper, the explicit equation of the single diode model (SDM) expressed by the Lambert W function was reduced to its simplified form through variable replacement; then the simplified explicit equation was combined with an intelligent optimization algorithm to estimate the SDM parameters of solar cells and PV modules. To evaluate the parameter extraction performance of the new method, eight typical intelligent optimization algorithms were combined with the implicit, explicit, and simplified explicit equation to extract the SDM parameters of a solar cell and three types of PV modules. The results show that the new method not only improves the accuracy of parameter extraction but also enhances the robustness and convergence speed. Most importantly, the new method can nearly improve the parameter extraction accuracy of a poor-performing algorithm in traditional methods to the level of other well-performing algorithms without enhancing the algorithm itself. In a word, this study offers a new choice for a more accurate and reliable extraction of SDM parameters from both solar cells and PV modules.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5425-:d:1195877
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5425/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/14/5425/
    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. 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.
    3. 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.
    4. 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.
    5. Tong, Nhan Thanh & Pora, Wanchalerm, 2016. "A parameter extraction technique exploiting intrinsic properties of solar cells," Applied Energy, Elsevier, vol. 176(C), pages 104-115.
    6. Toledo, F.J. & Blanes, José M., 2016. "Analytical and quasi-explicit four arbitrary point method for extraction of solar cell single-diode model parameters," Renewable Energy, Elsevier, vol. 92(C), pages 346-356.
    7. Chellaswamy, C. & Ramesh, R., 2016. "Parameter extraction of solar cell models based on adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 97(C), pages 823-837.
    8. Chen, Yifeng & Wang, Xuemeng & Li, Da & Hong, Ruijiang & Shen, Hui, 2011. "Parameters extraction from commercial solar cells I-V characteristics and shunt analysis," Applied Energy, Elsevier, vol. 88(6), pages 2239-2244, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomas Ruzgas & Irma Jankauskienė & Audrius Zajančkauskas & Mantas Lukauskas & Matas Bazilevičius & Rugilė Kaluževičiūtė & Jurgita Arnastauskaitė, 2024. "Solving Linear and Nonlinear Delayed Differential Equations Using the Lambert W Function for Economic and Biological Problems," Mathematics, MDPI, vol. 12(17), pages 1-15, September.
    2. Andreea Sabadus & Nicoleta Stefu & Marius Paulescu, 2024. "Evaluating Outdoor Performance of PV Modules Using an Innovative Explicit One-Diode Model," Energies, MDPI, vol. 17(11), pages 1-12, May.
    3. Cheng Qin & Jianing Li & Chen Yang & Bin Ai & Yecheng Zhou, 2024. "Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining Metaheuristic Algorithms with Different Simulation Current Calculation Methods," Energies, MDPI, vol. 17(10), pages 1-32, May.

    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. Słowik, Adam & Cpałka, Krzysztof & Xue, Yu & Hapka, Aneta, 2024. "An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm," Applied Energy, Elsevier, vol. 364(C).
    2. 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).
    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. Nunes, H.G.G. & Pombo, J.A.N. & Mariano, S.J.P.S. & Calado, M.R.A. & Felippe de Souza, J.A.M., 2018. "A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization," Applied Energy, Elsevier, vol. 211(C), pages 774-791.
    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. Choulli, Imade & Elyaqouti, Mustapha & Arjdal, El hanafi & Ben hmamou, Dris & Saadaoui, Driss & Lidaighbi, Souad & Elhammoudy, Abdelfattah & Abazine, Ismail, 2023. "Hybrid optimization based on the analytical approach and the particle swarm optimization algorithm (Ana-PSO) for the extraction of single and double diode models parameters," Energy, Elsevier, vol. 283(C).
    7. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2019. "Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm," Applied Energy, Elsevier, vol. 250(C), pages 109-117.
    8. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.
    9. 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.
    10. 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.
    11. Bushra Shakir Mahmood & Nazar K. Hussein & Mansourah Aljohani & Mohammed Qaraad, 2023. "A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction," Mathematics, MDPI, vol. 11(19), pages 1-40, October.
    12. Yousri, Dalia & Thanikanti, Sudhakar Babu & Allam, Dalia & Ramachandaramurthy, Vigna K. & Eteiba, M.B., 2020. "Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters," Energy, Elsevier, vol. 195(C).
    13. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2020. "Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization," Energy, Elsevier, vol. 195(C).
    14. Zhang, Yiying & Ma, Maode & Jin, Zhigang, 2020. "Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models," Energy, Elsevier, vol. 211(C).
    15. 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.
    16. Chen, Xu & Yue, Hong & Yu, Kunjie, 2019. "Perturbed stochastic fractal search for solar PV parameter estimation," Energy, Elsevier, vol. 189(C).
    17. 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.
    18. 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.
    19. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    20. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.

    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:jeners:v:16:y:2023:i:14:p:5425-:d:1195877. 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.