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Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision

Author

Listed:
  • Moath Alsafasfeh

    (Electrical and Computer Engineering Department, College of Engineering and Applied Sciences, Western Michigan University, Kalamazoo, MI 49001, USA)

  • Ikhlas Abdel-Qader

    (Electrical and Computer Engineering Department, College of Engineering and Applied Sciences, Western Michigan University, Kalamazoo, MI 49001, USA)

  • Bradley Bazuin

    (Electrical and Computer Engineering Department, College of Engineering and Applied Sciences, Western Michigan University, Kalamazoo, MI 49001, USA)

  • Qais Alsafasfeh

    (Energy Engineering Departments, College of Engineering, Al Hussein Technical University, Amman 25175, Jordan; Sabbatical leave from Tafila Technical University, Department of Electrical power and Mechatronics, Tafila-Jordan)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48121, USA)

Abstract

One of the most important sources of clean energy in the future is expected to be solar energy which is considered a real time source. Research efforts to optimize solar energy utilization are mainly concentrated on the components of solar energy systems. Photovoltaic (PV) modules are considered the main components of solar energy systems and PVs’ operations typically occur without any supervisory mechanisms, which means many external and/or internal obstacles can occur and hinder a system’s efficiency. To avoid these problems, the paper presents a system to address and detect the faults in a PV system by providing a safer and more time efficient inspection system in real time. In this paper, we proposing a real time inspection and fault detection system for PV modules. The system has two cameras, a thermal and a Charge-Coupled Device CCD. They are mounted on a drone and they used to capture the scene of the PV modules simultaneously while the drone is flying over the solar garden. A mobile PV system has been constructed primarily to validate our real time proposed system and for the proposed method in the Digital Image and Signal Processing Laboratory (DISPLAY) at Western Michigan University (WMU). Defects have been detected accurately in the PV modules using the proposed real time system. As a result, the proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude.

Suggested Citation

  • Moath Alsafasfeh & Ikhlas Abdel-Qader & Bradley Bazuin & Qais Alsafasfeh & Wencong Su, 2018. "Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision," Energies, MDPI, vol. 11(9), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2252-:d:166079
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    References listed on IDEAS

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    1. Henrik Zsiborács & Gábor Pintér & Attila Bai & József Popp & Zoltán Gabnai & Béla Pályi & István Farkas & Nóra Hegedűsné Baranyai & Christian Gützer & Heidelinde Trimmel & Sandro Oswald & Philipp Weih, 2018. "Comparison of Thermal Models for Ground-Mounted South-Facing Photovoltaic Technologies: A Practical Case Study," Energies, MDPI, vol. 11(5), pages 1-18, May.
    2. Henrik Zsiborács & Nóra Hegedűsné Baranyai & András Vincze & István Háber & Gábor Pintér, 2018. "Economic and Technical Aspects of Flexible Storage Photovoltaic Systems in Europe," Energies, MDPI, vol. 11(6), pages 1-17, June.
    3. Gábor Pintér & Nóra Hegedűsné Baranyai & Alec Wiliams & Henrik Zsiborács, 2018. "Study of Photovoltaics and LED Energy Efficiency: Case Study in Hungary," Energies, MDPI, vol. 11(4), pages 1-13, March.
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    Cited by:

    1. Gábor Pintér, 2020. "The Potential Role of Power-to-Gas Technology Connected to Photovoltaic Power Plants in the Visegrad Countries—A Case Study," Energies, MDPI, vol. 13(23), pages 1-14, December.
    2. Giovanni Cipriani & Antonino D’Amico & Stefania Guarino & Donatella Manno & Marzia Traverso & Vincenzo Di Dio, 2020. "Convolutional Neural Network for Dust and Hotspot Classification in PV Modules," Energies, MDPI, vol. 13(23), pages 1-17, December.
    3. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.
    4. 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).
    5. Nóra Hegedűsné Baranyai & Henrik Zsiborács & András Vincze & Nóra Rodek & Martina Makai & Gábor Pintér, 2021. "Correlation Analysis of the Spread of Household-Sized Photovoltaic Power Plants and Various District Indicators: A Case Study," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    6. Min-Gwan Kim & Siheon Jeong & Seok-Tae Kim & Ki-Yong Oh, 2023. "Anomaly Detection of Underground Transmission-Line through Multiscale Mask DCNN and Image Strengthening," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
    7. Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    8. 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).
    9. Qais Alsafasfeh & Omar A. Saraereh & Imran Khan & Sunghwan Kim, 2019. "Solar PV Grid Power Flow Analysis," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    10. Sharmarke Hassan & Mahmoud Dhimish, 2022. "Review of Current State-of-the-Art Research on Photovoltaic Soiling, Anti-Reflective Coating, and Solar Roads Deployment Supported by a Pilot Experiment on a PV Road," Energies, MDPI, vol. 15(24), pages 1-24, December.
    11. Gábor Pintér & Henrik Zsiborács & Nóra Hegedűsné Baranyai & András Vincze & Zoltán Birkner, 2020. "The Economic and Geographical Aspects of the Status of Small-Scale Photovoltaic Systems in Hungary—A Case Study," Energies, MDPI, vol. 13(13), pages 1-22, July.
    12. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    13. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    14. Georgios Goudelis & Pavlos I. Lazaridis & Mahmoud Dhimish, 2022. "A Review of Models for Photovoltaic Crack and Hotspot Prediction," Energies, MDPI, vol. 15(12), pages 1-24, June.

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