IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v161y2022ics1364032122002647.html
   My bibliography  Save this article

Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies

Author

Listed:
  • Høiaas, Ingeborg
  • Grujic, Katarina
  • Imenes, Anne Gerd
  • Burud, Ingunn
  • Olsen, Espen
  • Belbachir, Nabil

Abstract

The massive growth of PV farms, both in number and size, has motivated new approaches in inspection system design and monitoring. This paper presents a review of imaging technologies and methods for analysis and characterization of faults in photovoltaic (PV) modules. The paper provides a brief overview of PV system (PVS) reliability studies and monitoring approaches where fault related PVS power loss is evaluated. Research on infrared thermography (IRT) and luminescence imaging technologies is thoroughly reviewed, with focus on ease of implementation, efficiency and unmanned aerial system (UAS) compatibility. Furthermore, the review will provide novel insight into state-of-the-art electroluminescence (EL), photoluminescence (PL) and ultraviolet fluorescence (UVF) imaging, and how to interpret these images. The development of imaging techniques will continue to be an attractive domain of research that can be combined with aerial scanning for a cost-effective remote inspection that enable reliable power production in large-scale PV plants.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:161:y:2022:i:c:s1364032122002647
    DOI: 10.1016/j.rser.2022.112353
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122002647
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2022.112353?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Tsanakas, John A. & Ha, Long D. & Al Shakarchi, F., 2017. "Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery," Renewable Energy, Elsevier, vol. 102(PA), pages 224-233.
    2. 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.
    3. Muehleisen, Wolfgang & Eder, Gabriele C. & Voronko, Yuliya & Spielberger, Markus & Sonnleitner, Horst & Knoebl, Karl & Ebner, Rita & Ujvari, Gusztav & Hirschl, Christina, 2018. "Outdoor detection and visualization of hailstorm damages of photovoltaic plants," Renewable Energy, Elsevier, vol. 118(C), pages 138-145.
    4. Triki-Lahiani, Asma & Bennani-Ben Abdelghani, Afef & Slama-Belkhodja, Ilhem, 2018. "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2680-2692.
    5. 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.
    6. Papargyri, Lamprini & Theristis, Marios & Kubicek, Bernhard & Krametz, Thomas & Mayr, Christoph & Papanastasiou, Panos & Georghiou, George E., 2020. "Modelling and experimental investigations of microcracks in crystalline silicon photovoltaics: A review," Renewable Energy, Elsevier, vol. 145(C), pages 2387-2408.
    7. Woo Gyun Shin & Suk Whan Ko & Hyung Jun Song & Young Chul Ju & Hye Mi Hwang & Gi Hwan Kang, 2018. "Origin of Bypass Diode Fault in c-Si Photovoltaic Modules: Leakage Current under High Surrounding Temperature," Energies, MDPI, vol. 11(9), pages 1-11, September.
    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. Mik, Krzysztof & Bugaj, Marcin & Chaja, Patryk, 2021. "The evaluation of the snail track affected photovoltaic modules by different methods after 3-year operating in central Poland," Renewable Energy, Elsevier, vol. 163(C), pages 504-516.
    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. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Monitoring system for photovoltaic plants: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1180-1207.
    12. Dolara, Alberto & Lazaroiu, George Cristian & Leva, Sonia & Manzolini, Giampaolo, 2013. "Experimental investigation of partial shading scenarios on PV (photovoltaic) modules," Energy, Elsevier, vol. 55(C), pages 466-475.
    13. Cabrera-Tobar, Ana & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Gomis-Bellmunt, Oriol, 2016. "Topologies for large scale photovoltaic power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 309-319.
    14. Isidoro Lillo-Bravo & Pablo González-Martínez & Miguel Larrañeta & José Guasumba-Codena, 2018. "Impact of Energy Losses Due to Failures on Photovoltaic Plant Energy Balance," Energies, MDPI, vol. 11(2), pages 1-23, February.
    15. Maghami, Mohammad Reza & Hizam, Hashim & Gomes, Chandima & Radzi, Mohd Amran & Rezadad, Mohammad Ismael & Hajighorbani, Shahrooz, 2016. "Power loss due to soiling on solar panel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1307-1316.
    16. Han, Changwoon & Lee, Hyeonseok, 2018. "Investigation and modeling of long-term mismatch loss of photovoltaic array," Renewable Energy, Elsevier, vol. 121(C), pages 521-527.
    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. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).
    2. Alberto Redondo Plaza & Victor Ndeti Ngungu & Sara Gallardo Saavedra & José Ignacio Morales Aragonés & Víctor Alonso Gómez & Lilian Johanna Obregón & Luis Hernández Callejo, 2023. "Partial Photoluminescence Imaging for Inspection of Photovoltaic Cells: Artificial LED Excitation and Sunlight Excitation," Energies, MDPI, vol. 16(11), pages 1-12, June.
    3. Adnan Aslam & Naseer Ahmed & Safian Ahmed Qureshi & Mohsen Assadi & Naveed Ahmed, 2022. "Advances in Solar PV Systems; A Comprehensive Review of PV Performance, Influencing Factors, and Mitigation Techniques," Energies, MDPI, vol. 15(20), pages 1-52, October.
    4. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).

    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. 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).
    2. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    3. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).
    4. Jae-Sub Ko & Dae-Kyong Kim, 2021. "Localization of Disconnection Faults in PV Installations Using the Multiple Frequencies Injection Method," Energies, MDPI, vol. 14(21), pages 1-28, November.
    5. 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).
    6. 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.
    7. 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.
    8. Leonardo Cardinale-Villalobos & Carlos Meza & Abel Méndez-Porras & Luis D. Murillo-Soto, 2022. "Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study," Energies, MDPI, vol. 15(5), pages 1-20, March.
    9. Rahman, Md Momtazur & Khan, Imran & Alameh, Kamal, 2021. "Potential measurement techniques for photovoltaic module failure diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    10. 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).
    11. Rouani, Lahcene & Harkat, Mohamed Faouzi & Kouadri, Abdelmalek & Mekhilef, Saad, 2021. "Shading fault detection in a grid-connected PV system using vertices principal component analysis," Renewable Energy, Elsevier, vol. 164(C), pages 1527-1539.
    12. Georgios Goudelis & Pavlos I. Lazaridis & Mahmoud Dhimish, 2022. "A Review of Models for Photovoltaic Crack and Hotspot Prediction," Energies, MDPI, vol. 15(12), pages 1-24, June.
    13. Qu, Jiaqi & Qian, Zheng & Pei, Yan & Wei, Lu & Zareipour, Hamidreza & Sun, Qiang, 2022. "An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection," Applied Energy, Elsevier, vol. 319(C).
    14. Liu, Weidong & Jiang, Xiaohua & Li, Shaoshuai & Luo, Ji & Wen, Gen, 2020. "Photovoltaic module regional clustering in mainland China and application based on factors influencing field reliability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    15. Lee, Chung Geun & Shin, Woo Gyun & Lim, Jong Rok & Kang, Gi Hwan & Ju, Young Chul & Hwang, Hye Mi & Chang, Hyo Sik & Ko, Suk Whan, 2021. "Analysis of electrical and thermal characteristics of PV array under mismatching conditions caused by partial shading and short circuit failure of bypass diodes," Energy, Elsevier, vol. 218(C).
    16. Peinado Gonzalo, Alfredo & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Survey of maintenance management for photovoltaic power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    17. Carlos Toledo & Lucía Serrano-Lujan & Jose Abad & Antonio Lampitelli & Antonio Urbina, 2019. "Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization," Energies, MDPI, vol. 12(4), pages 1-20, February.
    18. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.
    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.

    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:rensus:v:161:y:2022:i:c:s1364032122002647. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.