IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2055-d769172.html
   My bibliography  Save this article

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Author

Listed:
  • Aline Kirsten Vidal de Oliveira

    (Department of Civil Engineering, Universidade Federal de Santa Catarina, Florianópolis 88054-700, Brazil)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Alesund, Norway
    Department of Physics and Energy Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran)

  • Ricardo Rüther

    (Department of Civil Engineering, Universidade Federal de Santa Catarina, Florianópolis 88054-700, Brazil)

Abstract

In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

Suggested Citation

  • Aline Kirsten Vidal de Oliveira & Mohammadreza Aghaei & Ricardo Rüther, 2022. "Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review," Energies, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2055-:d:769172
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2055/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2055/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tsanakas, John A. & Ha, Long & Buerhop, Claudia, 2016. "Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 695-709.
    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. Guilherme Souza & Ricardo Santos & Erlandson Saraiva, 2022. "A Log-Logistic Predictor for Power Generation in Photovoltaic Systems," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. Wiktor Olchowik & Marcin Bednarek & Tadeusz Dąbrowski & Adam Rosiński, 2023. "Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems," Energies, MDPI, vol. 16(18), pages 1-24, September.
    3. Cardoso, Andressa & Jurado-Rodríguez, David & López, Alfonso & Ramos, M. Isabel & Jurado, Juan Manuel, 2024. "Automated detection and tracking of photovoltaic modules from 3D remote sensing data," Applied Energy, Elsevier, vol. 367(C).
    4. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    5. Ning Zang & Yong Tao & Zuoteng Yuan & Chen Yuan & Bailin Jing & Renfeng Liu, 2024. "Rasterized Data Image Processing (RDIP) Techniques for Photovoltaic (PV) Data Cleaning and Application in Power Prediction," Energies, MDPI, vol. 17(12), pages 1-16, June.
    6. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.

    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. Segovia Ramírez, Isaac & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2022. "A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections," Renewable Energy, Elsevier, vol. 187(C), pages 371-389.
    2. 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.
    3. 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).
    4. 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).
    5. 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.
    6. Kyoik Choi & Jangwon Suh, 2023. "Fault Detection and Power Loss Assessment for Rooftop Photovoltaics Installed in a University Campus, by Use of UAV-Based Infrared Thermography," Energies, MDPI, vol. 16(11), pages 1-16, June.
    7. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.
    8. Santhakumari, Manju & Sagar, Netramani, 2019. "A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: Failure detection methods and essential mitigation techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 83-100.
    9. Pía Vásquez & Ignacia Devoto & Pablo Ferrada & Abel Taquichiri & Carlos Portillo & Rodrigo Palma-Behnke, 2021. "Inspection Data Collection Tool for Field Testing of Photovoltaic Modules in the Atacama Desert," Energies, MDPI, vol. 14(9), pages 1-24, April.
    10. Romênia G. Vieira & Fábio M. U. de Araújo & Mahmoud Dhimish & Maria I. S. Guerra, 2020. "A Comprehensive Review on Bypass Diode Application on Photovoltaic Modules," Energies, MDPI, vol. 13(10), pages 1-21, May.
    11. 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.
    12. Gallardo-Saavedra, Sara & Hernández-Callejo, Luis & Duque-Perez, Oscar, 2018. "Technological review of the instrumentation used in aerial thermographic inspection of photovoltaic plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 566-579.
    13. Oscar Kwame Segbefia & Tor Oskar Sætre, 2022. "Investigation of the Temperature Sensitivity of 20-Years Old Field-Aged Photovoltaic Panels Affected by Potential Induced Degradation," Energies, MDPI, vol. 15(11), pages 1-17, May.
    14. Hamid Iftikhar & Eduardo Sarquis & P. J. Costa Branco, 2021. "Why Can Simple Operation and Maintenance (O&M) Practices in Large-Scale Grid-Connected PV Power Plants Play a Key Role in Improving Its Energy Output?," Energies, MDPI, vol. 14(13), pages 1-29, June.
    15. Cubukcu, M. & Akanalci, A., 2020. "Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey," Renewable Energy, Elsevier, vol. 147(P1), pages 1231-1238.
    16. Zhang, Jinxia & Chen, Xinyi & Wei, Haikun & Zhang, Kanjian, 2024. "A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation," Applied Energy, Elsevier, vol. 355(C).
    17. Muhammad Rameez Javed & Zain Shabbir & Furqan Asghar & Waseem Amjad & Faisal Mahmood & Muhammad Omer Khan & Umar Siddique Virk & Aashir Waleed & Zunaib Maqsood Haider, 2022. "An Efficient Fault Detection Method for Induction Motors Using Thermal Imaging and Machine Vision," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    18. Han, Changwoon & Lee, Hyeonseok, 2019. "A field-applicable health monitoring method for photovoltaic system," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 219-227.
    19. Tamás Orosz & Anton Rassõlkin & Pedro Arsénio & Peter Poór & Daniil Valme & Ádám Sleisz, 2024. "Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels," Energies, MDPI, vol. 17(6), pages 1-22, March.
    20. Høiaas, Ingeborg & Grujic, Katarina & Imenes, Anne Gerd & Burud, Ingunn & Olsen, Espen & Belbachir, Nabil, 2022. "Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(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:jeners:v:15:y:2022:i:6:p:2055-:d:769172. 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.