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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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    1. Bressan, M. & Gutierrez, A. & Garcia Gutierrez, L. & Alonso, C., 2018. "Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems," Renewable Energy, Elsevier, vol. 127(C), pages 334-343.
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    5. 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.
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    4. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
    5. 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.
    6. 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).
    7. 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.
    8. Cruz-Rojas, Tonatiuh & Franco, Jesus Alejandro & Hernandez-Escobedo, Quetzalcoatl & Ruiz-Robles, Dante & Juarez-Lopez, Jose Manuel, 2023. "A novel comparison of image semantic segmentation techniques for detecting dust in photovoltaic panels using machine learning and deep learning," Renewable Energy, Elsevier, vol. 217(C).
    9. 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.
    10. 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.

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