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Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features.

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Názov: Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features.
Autori: Sridharan, Naveen Venkatesh, Sugumaran, Vaithiyanathan
Zdroj: Energy Sources Part A: Recovery, Utilization & Environmental Effects; Dec2025, Vol. 47 Issue 2, p1-17, 17p
Predmety: PHOTOVOLTAIC power generation, DECISION trees, DEEP learning, DRONE aircraft equipment & supplies, CLASSIFICATION, RANDOM forest algorithms, IMAGE recognition (Computer vision), INSPECTION & review
Abstrakt: Visual faults in photovoltaic (PV) modules persist as a problem that can create consequences such as reduced life span, increased output power loss and raising safety concerns during operation. Increased manpower requirement, larger time consumption, confinement to single fault prediction and high initial cost are certain drawbacks of conventional fault diagnosis techniques. Recent advancements in technology and the innovation of scientific techniques have urged the need for advanced fault diagnosis techniques that deliver instantaneous results. In the present study, unmanned aerial vehicles (UAVs) were employed to capture images of PVM with visual faults. The most common visual faults in photovoltaic modules (PVM) are delamination, burn marks, glass breakage, discoloration, and snail trails. Each fault condition contains a unique image pattern appearance attributed to the particular type of fault. Such patterns are extracted using convolutional neural networks and classified with the help of decision tree algorithms. First, the features are extracted from these aerial images by utilizing pre-trained AlexNet convolutional neural networks. Secondly, the J48 decision tree algorithm is utilized to select the most significant and valuable features from the extracted image features. Finally, the classification is carried out with several decision tree algorithms such as decision stump, hoeffiding tree, J48, linear model tree (LMT), random forest, random tree, representative (REP) tree, best first (BF) tree, extra tree, functional tree (FT), J48 consolidated, J48 graft, least absolute deviation (LAD) tree, naïve bayes (NB) tree and simple cart. The classification accuracies of the algorithms mentioned above are compared to suggest the best-in-class algorithm for real-time application. Among all the available tree-based algorithms, the random forest algorithm produced a maximum classification accuracy of 98.25% with a computational time of 0.89 seconds. [ABSTRACT FROM AUTHOR]
Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features.
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  Data: Energy Sources Part A: Recovery, Utilization & Environmental Effects; Dec2025, Vol. 47 Issue 2, p1-17, 17p
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  Data: <searchLink fieldCode="DE" term="%22PHOTOVOLTAIC+power+generation%22">PHOTOVOLTAIC power generation</searchLink><br /><searchLink fieldCode="DE" term="%22DECISION+trees%22">DECISION trees</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22DRONE+aircraft+equipment+%26+supplies%22">DRONE aircraft equipment & supplies</searchLink><br /><searchLink fieldCode="DE" term="%22CLASSIFICATION%22">CLASSIFICATION</searchLink><br /><searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22IMAGE+recognition+%28Computer+vision%29%22">IMAGE recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22INSPECTION+%26+review%22">INSPECTION & review</searchLink>
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  Data: Visual faults in photovoltaic (PV) modules persist as a problem that can create consequences such as reduced life span, increased output power loss and raising safety concerns during operation. Increased manpower requirement, larger time consumption, confinement to single fault prediction and high initial cost are certain drawbacks of conventional fault diagnosis techniques. Recent advancements in technology and the innovation of scientific techniques have urged the need for advanced fault diagnosis techniques that deliver instantaneous results. In the present study, unmanned aerial vehicles (UAVs) were employed to capture images of PVM with visual faults. The most common visual faults in photovoltaic modules (PVM) are delamination, burn marks, glass breakage, discoloration, and snail trails. Each fault condition contains a unique image pattern appearance attributed to the particular type of fault. Such patterns are extracted using convolutional neural networks and classified with the help of decision tree algorithms. First, the features are extracted from these aerial images by utilizing pre-trained AlexNet convolutional neural networks. Secondly, the J48 decision tree algorithm is utilized to select the most significant and valuable features from the extracted image features. Finally, the classification is carried out with several decision tree algorithms such as decision stump, hoeffiding tree, J48, linear model tree (LMT), random forest, random tree, representative (REP) tree, best first (BF) tree, extra tree, functional tree (FT), J48 consolidated, J48 graft, least absolute deviation (LAD) tree, naïve bayes (NB) tree and simple cart. The classification accuracies of the algorithms mentioned above are compared to suggest the best-in-class algorithm for real-time application. Among all the available tree-based algorithms, the random forest algorithm produced a maximum classification accuracy of 98.25% with a computational time of 0.89 seconds. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/15567036.2021.2020379
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 1
    Subjects:
      – SubjectFull: PHOTOVOLTAIC power generation
        Type: general
      – SubjectFull: DECISION trees
        Type: general
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: DRONE aircraft equipment & supplies
        Type: general
      – SubjectFull: CLASSIFICATION
        Type: general
      – SubjectFull: RANDOM forest algorithms
        Type: general
      – SubjectFull: IMAGE recognition (Computer vision)
        Type: general
      – SubjectFull: INSPECTION & review
        Type: general
    Titles:
      – TitleFull: Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features.
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            NameFull: Sridharan, Naveen Venkatesh
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            NameFull: Sugumaran, Vaithiyanathan
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          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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            – TitleFull: Energy Sources Part A: Recovery, Utilization & Environmental Effects
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