IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v102y2017ipap224-233.html
   My bibliography  Save this article

Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery

Author

Listed:
  • Tsanakas, John A.
  • Ha, Long D.
  • Al Shakarchi, F.

Abstract

Towards tackling the evident practical challenges of fault detection and diagnosis for PV modules, especially in large-scale installations, this paper proposes two different techniques for advanced inspection mapping of PV plants; aerial triangulation and terrestrial georeferencing. The former uses data of aerial thermal/visual imagery of operating PV modules, obtained by an unmanned aerial vehicle (UAV), to generate static “inspection maps”, in the form of true orthophoto mosaics. On the other hand, georeferencing is used to associate terrestrial thermal/visual imagery, obtained at distinct positions in a PV plant, with geographic data. By such way, inspection is based on a dynamic virtual map of the installation. Both mapping techniques were tested in two grid-connected PV systems, of a total installed power of 70.2 KWp. Several defective modules were easily and accurately detected, typically as abnormal temperature profiles, in the infrared (IR) spectrum. In addition, specific thermal image patterns of operating modules, were validated and quantified by additional diagnostic measurements, and were assigned to possible fault types. On the basis of the experience feedback, the potential of the proposed techniques and their limitations, for further application to PV plants of larger scale, are also discussed.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:102:y:2017:i:pa:p:224-233
    DOI: 10.1016/j.renene.2016.10.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116309119
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2016.10.046?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. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    2. Sharma, Vikrant & Chandel, S.S., 2013. "Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 753-767.
    3. Djordjevic, Sinisa & Parlevliet, David & Jennings, Philip, 2014. "Detectable faults on recently installed solar modules in Western Australia," Renewable Energy, Elsevier, vol. 67(C), pages 215-221.
    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. 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.
    2. 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.
    3. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    4. Miguel De Simón-Martín & Ana-María Diez-Suárez & Laura Álvarez-de Prado & Alberto González-Martínez & Álvaro De la Puente-Gil & Jorge Blanes-Peiró, 2017. "Development of a GIS Tool for High Precision PV Degradation Monitoring and Supervision: Feasibility Analysis in Large and Small PV Plants," Sustainability, MDPI, vol. 9(6), pages 1-29, June.
    5. Naveen Venkatesh Sridharan & Jerome Vasanth Joseph & Sugumaran Vaithiyanathan & Mohammadreza Aghaei, 2023. "Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules," Energies, MDPI, vol. 16(15), pages 1-17, August.
    6. Sridharan Naveen Venkatesh & Vaithiyanathan Sugumaran, 2022. "A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules," Journal of Risk and Reliability, , vol. 236(1), pages 148-159, February.
    7. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    8. 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).
    9. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    10. 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).
    11. 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.
    12. 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.
    13. Silva, Aline M. & Melo, Fernando C. & Reis, Joaquim H. & Freitas, Luiz C.G., 2019. "The study and application of evaluation methods for photovoltaic modules under real operational conditions, in a region of the Brazilian Southeast," Renewable Energy, Elsevier, vol. 138(C), pages 1189-1204.

    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. 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.
    2. Bouaichi, Abdellatif & Alami Merrouni, Ahmed & Hajjaj, Charaf & Messaoudi, Choukri & Ghennioui, Abdellatif & Benlarabi, Ahmed & Ikken, Badr & El Amrani, Aumeur & Zitouni, Houssin, 2019. "In-situ evaluation of the early PV module degradation of various technologies under harsh climatic conditions: The case of Morocco," Renewable Energy, Elsevier, vol. 143(C), pages 1500-1518.
    3. Atsu, Divine & Seres, Istvan & Aghaei, Mohammadreza & Farkas, Istvan, 2020. "Analysis of long-term performance and reliability of PV modules under tropical climatic conditions in sub-Saharan," Renewable Energy, Elsevier, vol. 162(C), pages 285-295.
    4. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    5. Zhang, Minhui & Zhang, Qin & Zhou, Dequn & Wang, Lei, 2021. "Punishment or reward? Strategies of stakeholders in the quality of photovoltaic plants based on evolutionary game analysis in China," Energy, Elsevier, vol. 220(C).
    6. Vimpari, Jussi & Junnila, Seppo, 2017. "Evaluating decentralized energy investments: Spatial value of on-site PV electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1217-1222.
    7. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    8. Kahoul, Nabil & Chenni, Rachid & Cheghib, Hocine & Mekhilef, Saad, 2017. "Evaluating the reliability of crystalline silicon photovoltaic modules in harsh environment," Renewable Energy, Elsevier, vol. 109(C), pages 66-72.
    9. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2020. "Field Analysis of three different silicon-based Technologies in Composite Climate Condition – Part II – Seasonal assessment and performance degradation rates using statistical tools," Renewable Energy, Elsevier, vol. 147(P1), pages 2102-2117.
    10. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2016. "A pilot facility for analysis and simulation of smart microgrids feeding smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1247-1255.
    11. Taghizadeh-Hesary, Farhad & Yoshino, Naoyuki & Inagaki, Yugo & Morgan, Peter J., 2021. "Analyzing the factors influencing the demand and supply of solar modules in Japan – Does financing matter," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 1-12.
    12. Essah, Emmanuel A. & Rodriguez Arguelles, Ana & Glover, Neil, 2015. "Assessing the performance of a building integrated BP c-Si PV system," Renewable Energy, Elsevier, vol. 73(C), pages 36-45.
    13. Cook, Tyson & Shaver, Lee & Arbaje, Paul, 2018. "Modeling constraints to distributed generation solar photovoltaic capacity installation in the US Midwest," Applied Energy, Elsevier, vol. 210(C), pages 1037-1050.
    14. Figgis, Benjamin & Ennaoui, Ahmed & Ahzi, Said & Rémond, Yves, 2017. "Review of PV soiling particle mechanics in desert environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 872-881.
    15. Akinyele, D.O. & Rayudu, R.K., 2016. "Community-based hybrid electricity supply system: A practical and comparative approach," Applied Energy, Elsevier, vol. 171(C), pages 608-628.
    16. Chandel, S.S. & Nagaraju Naik, M. & Sharma, Vikrant & Chandel, Rahul, 2015. "Degradation analysis of 28 year field exposed mono-c-Si photovoltaic modules of a direct coupled solar water pumping system in western Himalayan region of India," Renewable Energy, Elsevier, vol. 78(C), pages 193-202.
    17. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    18. Ranjbaran, Parisa & Yousefi, Hossein & Gharehpetian, G.B. & Astaraei, Fatemeh Razi, 2019. "A review on floating photovoltaic (FPV) power generation units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 332-347.
    19. Chao Huang & Michael Edesess & Alain Bensoussan & Kwok L. Tsui, 2016. "Performance Analysis of a Grid-Connected Upgraded Metallurgical Grade Silicon Photovoltaic System," Energies, MDPI, vol. 9(5), pages 1-15, May.
    20. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.

    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:102:y:2017:i:pa:p:224-233. 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.