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

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  • 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.

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  • 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
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    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.
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    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. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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).

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