IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i13p3214-d1425858.html

Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model

Author

Listed:
  • Chao-Ming Huang

    (Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan)

  • Shin-Ju Chen

    (Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan)

  • Sung-Pei Yang

    (Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan)

  • Yann-Chang Huang

    (Department of Electrical Engineering, Cheng Shiu University, Kaohsiung 833, Taiwan)

  • Pao-Yuan Huang

    (Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan)

Abstract

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference.

Suggested Citation

  • Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Yann-Chang Huang & Pao-Yuan Huang, 2024. "Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model," Energies, MDPI, vol. 17(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3214-:d:1425858
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/13/3214/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/13/3214/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manish Kumar Singla & Jyoti Gupta & Parag Nijhawan & Parminder Singh & Nimay Chandra Giri & Essam Hendawi & Mohamed I. Abu El-Sebah, 2023. "Parameter Estimation Techniques for Photovoltaic System Modeling," Energies, MDPI, vol. 16(17), pages 1-16, August.
    2. van Dyk, E.E. & Meyer, E.L., 2004. "Analysis of the effect of parasitic resistances on the performance of photovoltaic modules," Renewable Energy, Elsevier, vol. 29(3), pages 333-344.
    3. Bressan, M. & El Basri, Y. & Galeano, A.G. & Alonso, C., 2016. "A shadow fault detection method based on the standard error analysis of I-V curves," Renewable Energy, Elsevier, vol. 99(C), pages 1181-1190.
    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. Carrero, C. & Rodríguez, J. & Ramírez, D. & Platero, C., 2010. "Simple estimation of PV modules loss resistances for low error modelling," Renewable Energy, Elsevier, vol. 35(5), pages 1103-1108.
    2. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.
    3. Carrero, C. & Ramirez, D. & Rodríguez, J. & Castillo-Sierra, R., 2021. "Sensitivity analysis of loss resistances variations of PV generators applied to the assessment of maximum power point changes due to degradation," Renewable Energy, Elsevier, vol. 173(C), pages 351-361.
    4. Odysseas Tsafarakis & Kostas Sinapis & Wilfried G. J. H. M. van Sark, 2019. "A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 12(9), pages 1-18, May.
    5. Ghani, F. & Rosengarten, G. & Duke, M. & Carson, J.K., 2014. "The numerical calculation of single-diode solar-cell modelling parameters," Renewable Energy, Elsevier, vol. 72(C), pages 105-112.
    6. 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.
    7. Pillai, Dhanup S. & Rajasekar, N., 2018. "Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3503-3525.
    8. Rajput, Pramod & Shyam, & Tomar, Vivek & Tiwari, G.N. & Sastry, O.S. & Bhatti, T.S., 2018. "A thermal model for N series connected glass/cell/polymer sheet and glass/cell/glass crystalline silicon photovoltaic modules with hot solar cells connected in series and its thermal losses in real outdoor condition," Renewable Energy, Elsevier, vol. 126(C), pages 370-386.
    9. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    10. Mariana Durango-Flórez & Daniel González-Montoya & Luz Adriana Trejos-Grisales & Carlos Andres Ramos-Paja, 2022. "PV Array Reconfiguration Based on Genetic Algorithm for Maximum Power Extraction and Energy Impact Analysis," Sustainability, MDPI, vol. 14(7), pages 1-14, March.
    11. Cuce, Erdem & Cuce, Pinar Mert & Bali, Tulin, 2013. "An experimental analysis of illumination intensity and temperature dependency of photovoltaic cell parameters," Applied Energy, Elsevier, vol. 111(C), pages 374-382.
    12. Meng, Xian-long & Li, Xin-yuan & Liu, Cunliang & Zhang, Pu & Cheng, Ke, 2025. "Output performance of optimized photovoltaic array configurations exposed on nonuniform laser spot," Energy, Elsevier, vol. 318(C).
    13. Alonso Gutiérrez Galeano & Michael Bressan & Fernando Jiménez Vargas & Corinne Alonso, 2018. "Shading Ratio Impact on Photovoltaic Modules and Correlation with Shading Patterns," Energies, MDPI, vol. 11(4), pages 1-26, April.
    14. Torres-Ramírez, M. & Nofuentes, G. & Silva, J.P. & Silvestre, S. & Muñoz, J.V., 2014. "Study on analytical modelling approaches to the performance of thin film PV modules in sunny inland climates," Energy, Elsevier, vol. 73(C), pages 731-740.
    15. Kichou, Sofiane & Silvestre, Santiago & Nofuentes, Gustavo & Torres-Ramírez, Miguel & Chouder, Aissa & Guasch, Daniel, 2016. "Characterization of degradation and evaluation of model parameters of amorphous silicon photovoltaic modules under outdoor long term exposure," Energy, Elsevier, vol. 96(C), pages 231-241.
    16. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    17. Carrero, C. & Amador, J. & Arnaltes, S., 2007. "A single procedure for helping PV designers to select silicon PV modules and evaluate the loss resistances," Renewable Energy, Elsevier, vol. 32(15), pages 2579-2589.
    18. Bressan, M. & Gutierrez, A. & Garcia Gutierrez, L. & Alonso, C., 2018. "Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems," Renewable Energy, Elsevier, vol. 127(C), pages 334-343.
    19. Shen, Yu & He, Zengxiang & Xu, Zhen & Wang, Yiye & Li, Chenxi & Zhang, Jinxia & Zhang, Kanjian & Wei, Haikun, 2022. "Modeling of photovoltaic modules under common shading conditions," Energy, Elsevier, vol. 256(C).
    20. Safdar Mehmood & Yang Xia & Furong Qu & Meng He, 2023. "Investigating the Performance of Efficient and Stable Planer Perovskite Solar Cell with an Effective Inorganic Carrier Transport Layer Using SCAPS-1D Simulation," Energies, MDPI, vol. 16(21), pages 1-14, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2024:i:13:p:3214-:d:1425858. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.