IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v248y2025ics0960148125006524.html

Online crack detection on photovoltaic devices using a dynamic response analysis

Author

Listed:
  • Cárdenas-Bravo, Carlos
  • Cortés-Severino, Rodrigo
  • Morales, Felipe
  • Barraza, Rodrigo
  • Sánchez-Squella, Antonio
  • Valdivia-Lefort, Patricio

Abstract

This study presents a method to detect cracks in solar photovoltaic modules by analyzing their dynamic electrical response without interrupting operation. The approach evaluates indicators like settling time and damping coefficient using dynamic current and voltage measurements. Baseline assessments rely on electroluminescence imaging and I–V curve analysis. A DC/DC converter generates transient responses, and outdoor tests under stable irradiance confirm the method’s reliability, achieving a correlation coefficient above 0.89. Results show that cracks affect the damping coefficient in both current and voltage. Cracked modules exhibit a damping coefficient notably different from healthy ones. A linear dynamic electrical model supports this, showing healthy modules have a more oscillatory response. This method enables real-time, non-intrusive fault detection in PV modules, offering a practical solution for continuous health monitoring in solar energy systems. Its effectiveness across varying temperatures and irradiance suggests broad applicability in real-world conditions. Future research should address nonlinear aspects of the transient response, extend testing to diverse conditions, and integrate this method with current diagnostic techniques to improve accuracy. Additionally, incorporating advanced signal processing and machine learning could further enhance its ability to identify faults.

Suggested Citation

  • Cárdenas-Bravo, Carlos & Cortés-Severino, Rodrigo & Morales, Felipe & Barraza, Rodrigo & Sánchez-Squella, Antonio & Valdivia-Lefort, Patricio, 2025. "Online crack detection on photovoltaic devices using a dynamic response analysis," Renewable Energy, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:renene:v:248:y:2025:i:c:s0960148125006524
    DOI: 10.1016/j.renene.2025.122990
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125006524
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.122990?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    3. Tingting Pei & Xiaohong Hao, 2019. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation," Energies, MDPI, vol. 12(9), pages 1-16, May.
    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. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Gomathy Balasubramani & Venkatesan Thangavelu & Muniraj Chinnusamy & Umashankar Subramaniam & Sanjeevikumar Padmanaban & Lucian Mihet-Popa, 2020. "Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation," Energies, MDPI, vol. 13(6), pages 1-14, March.
    3. Saeedreza Jadidi & Hamed Badihi & Youmin Zhang, 2020. "Passive Fault-Tolerant Control Strategies for Power Converter in a Hybrid Microgrid," Energies, MDPI, vol. 13(21), pages 1-28, October.
    4. Hamza, Ali & Ali, Zunaib & Dudley, Sandra & Saleem, Komal & Uneeb, Muhammad & Christofides, Nicholas, 2025. "A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems," Applied Energy, Elsevier, vol. 393(C).
    5. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    6. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    7. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
    8. Ahmad Rivai & Nasrudin Abd Rahim & Mohamad Fathi Mohamad Elias & Jafferi Jamaludin, 2019. "Analysis of Photovoltaic String Failure and Health Monitoring with Module Fault Identification," Energies, MDPI, vol. 13(1), pages 1-16, December.
    9. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    10. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    11. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    12. Pratt, Lawrence & Govender, Devashen & Klein, Richard, 2021. "Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation," Renewable Energy, Elsevier, vol. 178(C), pages 1211-1222.
    13. Shirzad, Hossein & Barati, Ali Akbar & Ehteshammajd, Shaghayegh & Goli, Imaneh & Siamian, Narges & Moghaddam, Saghi Movahhed & Pour, Mahdad & Tan, Rong & Janečková, Kristina & Sklenička, Petr & Azadi,, 2022. "Agricultural land tenure system in Iran: An overview," Land Use Policy, Elsevier, vol. 123(C).
    14. Guillermo Almonacid-Olleros & Gabino Almonacid & David Gil & Javier Medina-Quero, 2022. "Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates," Sustainability, MDPI, vol. 14(5), pages 1-15, March.
    15. Chang, Zhonghao & Han, Te, 2024. "Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(C).
    16. Gong, Bin & An, Aimin & Shi, Yaoke & Guan, Haijiao & Jia, Wenchao & Yang, Fazhi, 2024. "An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction," Energy, Elsevier, vol. 308(C).
    17. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    18. Dhimish, Mahmoud & Ahmad, Ameer & Tyrrell, Andy M., 2022. "Inequalities in photovoltaics modules reliability: From packaging to PV installation site," Renewable Energy, Elsevier, vol. 192(C), pages 805-814.
    19. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    20. Thitiphat Klinsuwan & Wachiraphong Ratiphaphongthon & Rabian Wangkeeree & Rattanaporn Wangkeeree & Chatchai Sirisamphanwong, 2023. "Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems," Energies, MDPI, vol. 16(4), pages 1-22, February.

    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:eee:renene:v:248:y:2025:i:c:s0960148125006524. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.