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Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images

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  • Roberto Pierdicca

    (Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Marina Paolanti

    (Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Andrea Felicetti

    (Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Fabio Piccinini

    (Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Primo Zingaretti

    (Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy)

Abstract

Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentation, making it useful for the automated inspection task. The proposed system is trained and evaluated on the photovoltaic thermal images dataset, a publicly available dataset collected for this work. Furthermore, the performances of three state-of-art deep neural networks, (DNNs) including UNet, FPNet and LinkNet, are compared and evaluated. Results show the effectiveness and the suitability of the proposed approach in terms of intersection over union (IoU) and the Dice coefficient.

Suggested Citation

  • Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6496-:d:459068
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    References listed on IDEAS

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    1. Sharma, Vikrant & Chandel, S.S., 2013. "Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 753-767.
    2. Fabio Piccinini & Roberto Pierdicca & Eva Savina Malinverni, 2020. "A Relational Conceptual Model in GIS for the Management of Photovoltaic Systems," Energies, MDPI, vol. 13(11), pages 1-21, June.
    3. 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:

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    2. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    3. Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    4. Juan-Pablo Villegas-Ceballos & Mateo Rico-Garcia & Carlos Andres Ramos-Paja, 2022. "Dataset for Detecting the Electrical Behavior of Photovoltaic Panels from RGB Images," Data, MDPI, vol. 7(6), pages 1-12, June.
    5. Ben Qi & Jingang Liang & Jiejuan Tong, 2023. "Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective," Energies, MDPI, vol. 16(4), pages 1-27, February.
    6. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    7. 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.
    8. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.
    9. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).

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