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Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Author

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  • Aline Kirsten Vidal de Oliveira

    (Department of Civil Engineering, Universidade Federal de Santa Catarina, Florianópolis 88054-700, Brazil)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Alesund, Norway
    Department of Physics and Energy Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran)

  • Ricardo Rüther

    (Department of Civil Engineering, Universidade Federal de Santa Catarina, Florianópolis 88054-700, Brazil)

Abstract

In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

Suggested Citation

  • Aline Kirsten Vidal de Oliveira & Mohammadreza Aghaei & Ricardo Rüther, 2022. "Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review," Energies, MDPI, vol. 15(6), pages 1-24, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2055-:d:769172
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Guilherme Souza & Ricardo Santos & Erlandson Saraiva, 2022. "A Log-Logistic Predictor for Power Generation in Photovoltaic Systems," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. Wiktor Olchowik & Marcin Bednarek & Tadeusz Dąbrowski & Adam Rosiński, 2023. "Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems," Energies, MDPI, vol. 16(18), pages 1-24, September.
    3. 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).
    4. Ning Zang & Yong Tao & Zuoteng Yuan & Chen Yuan & Bailin Jing & Renfeng Liu, 2024. "Rasterized Data Image Processing (RDIP) Techniques for Photovoltaic (PV) Data Cleaning and Application in Power Prediction," Energies, MDPI, vol. 17(12), pages 1-16, June.
    5. 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.

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