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A Comparison and Introduction of Novel Solar Panel’s Fault Diagnosis Technique Using Deep-Features Shallow-Classifier through Infrared Thermography

Author

Listed:
  • Waqas Ahmed

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Muhammad Umair Ali

    (Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • M. A. Parvez Mahmud

    (School of Electrical Mechanical and Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia)

  • Kamran Ali Khan Niazi

    (Department of Mechanical and Production Engineering, Aarhus University, 8000 Aarhus, Denmark)

  • Amad Zafar

    (Department of Intelligent Mechatronics, Sejong University, Seoul 05006, Republic of Korea)

  • Tamas Kerekes

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Solar photovoltaics (PV) are susceptible to environmental and operational stresses due to their operation in an open atmosphere. Early detection and treatment of stress prevents hotspots and the total failure of solar panels. In response, the literature has proposed several approaches, each with its own limitations, such as high processing system requirements, large amounts of memory, long execution times, fewer types of faults diagnosed, failure to extract relevant features, and so on. Therefore, this research proposes a fast framework with the least memory and computing system requirements for the six different faults of a solar panel. Infrared thermographs from solar panels are fed into intense and architecturally complex deep convolutional networks capable of differentiating one million images into 1000 classes. Features without backpropagation are calculated to reduce execution time. Afterward, deep features are fed to shallow classifiers due to their fast training time. The proposed approach trains the shallow classifier in approximately 13 s with 95.5% testing accuracy. The approach is validated by manually extracting thermograph features and through the transfer of learned deep neural network approaches in terms of accuracy and speed. The proposed method is also compared with other existing methods.

Suggested Citation

  • Waqas Ahmed & Muhammad Umair Ali & M. A. Parvez Mahmud & Kamran Ali Khan Niazi & Amad Zafar & Tamas Kerekes, 2023. "A Comparison and Introduction of Novel Solar Panel’s Fault Diagnosis Technique Using Deep-Features Shallow-Classifier through Infrared Thermography," Energies, MDPI, vol. 16(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1043-:d:1039079
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    References listed on IDEAS

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    1. Gabriele C. Eder & Yuliya Voronko & Christina Hirschl & Rita Ebner & Gusztáv Újvári & Wolfgang Mühleisen, 2018. "Non-Destructive Failure Detection and Visualization of Artificially and Naturally Aged PV Modules," Energies, MDPI, vol. 11(5), pages 1-14, April.
    2. Waqas Ahmed & Jamil Ahmed Sheikh & Shahjadi Hisan Farjana & M. A. Parvez Mahmud, 2021. "Defects Impact on PV System GHG Mitigation Potential and Climate Change," Sustainability, MDPI, vol. 13(14), pages 1-9, July.
    3. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    4. 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.
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