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Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure

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  • Huerta Herraiz, Álvaro
  • Pliego Marugán, Alberto
  • García Márquez, Fausto Pedro

Abstract

The size and the complexity of photovoltaic solar power plants are increasing, and it requires an advanced and robust condition monitoring systems for ensuring their reliability. This paper proposes a novel method for faults detection in photovoltaic panels employing a thermographic camera embedded in an unmanned aerial vehicle. The large amount of data generated by these systems must be processed and analyzed. This paper presents a novel approach to identify panels to detect hot spots, and to set their locations. Two novels region-based convolutional neural networks are unified to generate a robust detection structure. The main contribution is the combination of thermography and telemetry data to provide a response of the panel condition monitoring. The data are acquired and then automatically processed, allowing fault detection during the inspection. A detailed description of the methodology is presented, including the different stages to build the neural networks, i.e. the training process, the acquisition and processing of data and the outcomes generation. A thermographic inspection of a real photovoltaic solar plant is done to validate the proposed methodology. The accuracy, the efficiency and the performance of the approach under different real scenarios are evaluated statistically obtaining satisfactory results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:153:y:2020:i:c:p:334-348
    DOI: 10.1016/j.renene.2020.01.148
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    References listed on IDEAS

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    3. Segovia Ramírez, Isaac & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2022. "A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections," Renewable Energy, Elsevier, vol. 187(C), pages 371-389.
    4. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    5. 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).
    6. 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.
    7. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan, 2020. "An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    8. Wei-Hsiang Chiang & Han-Sheng Wu & Jong-Shinn Wu & Shiow-Jyu Lin, 2022. "A Method for Estimating On-Field Photovoltaics System Efficiency Using Thermal Imaging and Weather Instrument Data and an Unmanned Aerial Vehicle," Energies, MDPI, vol. 15(16), pages 1-12, August.
    9. 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).
    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. 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.
    12. Di Tommaso, Antonio & Betti, Alessandro & Fontanelli, Giacomo & Michelozzi, Benedetto, 2022. "A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle," Renewable Energy, Elsevier, vol. 193(C), pages 941-962.
    13. Du, Bin & Lund, Peter D. & Wang, Jun, 2021. "Combining CFD and artificial neural network techniques to predict the thermal performance of all-glass straight evacuated tube solar collector," Energy, Elsevier, vol. 220(C).
    14. 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).
    15. Gabriella-Stefánia Szabó & Róbert Szabó & Loránd Szabó, 2022. "A Review of the Mitigating Methods against the Energy Conversion Decrease in Solar Panels," Energies, MDPI, vol. 15(18), pages 1-21, September.

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