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Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks

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  • Rico Espinosa, Alejandro
  • Bressan, Michael
  • Giraldo, Luis Felipe

Abstract

Physical fault detection in panels that are part of photovoltaic (PV) plants typically involves the analysis of thermal and electroluminescent images, which makes it either difficult or impossible to identify the source of the fault in the plant. This paper proposes a method of automatic physical fault classification for PV plants using convolutional neural networks for semantic segmentation and classification from RGB images. This study shows experimental results for 2 output classes identified as a fault and no fault, and 4 output classes as no fault, cracks, shadows, and dust that cannot be easily detected. The proposed method presents an average accuracy of 75% for 2 output classes and 70% for 4 classes, showing a positive approach to the proposed classification method for PV systems.

Suggested Citation

  • Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    • Handle: RePEc:eee:renene:v:162:y:2020:i:c:p:249-256
    DOI: 10.1016/j.renene.2020.07.154
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    References listed on IDEAS

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    5. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    6. Meng Xiao & Bo Yang & Shilong Wang & Yongsheng Chang & Song Li & Gang Yi, 2023. "Research on recognition methods of spot-welding surface appearances based on transfer learning and a lightweight high-precision convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2153-2170, June.
    7. 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).
    8. 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.
    9. G R Venkatakrishnan & R Rengaraj & S Tamilselvi & J Harshini & Ansheela Sahoo & C Ahamed Saleel & Mohamed Abbas & Erdem Cuce & C Jazlyn & Saboor Shaik & Pinar Mert Cuce & Saffa Riffat, 2023. "Detection, location, and diagnosis of different faults in large solar PV system—a review," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 659-674.
    10. Cavieres, Robinson & Barraza, Rodrigo & Estay, Danilo & Bilbao, José & Valdivia-Lefort, Patricio, 2022. "Automatic soiling and partial shading assessment on PV modules through RGB images analysis," Applied Energy, Elsevier, vol. 306(PA).

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