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An Enhanced Fault Classification Method for Photovoltaic Modules Using Texture Features.

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Title: An Enhanced Fault Classification Method for Photovoltaic Modules Using Texture Features.
Authors: Meng, Qiang1,2 (AUTHOR), Zhu, Shenji2,3 (AUTHOR), Wang, Dou2,3 (AUTHOR), Fan, Zeying3,4 (AUTHOR), Zheng, Chenghang1,4,5 (AUTHOR), Gao, Xiang1 (AUTHOR) xgao1@zju.edu.cn, Cen, Kefa1,2 (AUTHOR)
Source: Energies (19961073). Jan2026, Vol. 19 Issue 1, p131. 16p.
Subject Terms: *Photovoltaic power generation, *Surface texture, *Surface properties, *Statistical accuracy, *Electric power production, *Fault diagnosis, *Deep learning, *Image processing
Abstract: In the field of photovoltaic (PV) power generation, PV systems are prone to faults such as cells and cracks due to prolonged operation in complex environments, which reduce power generation efficiency and pose safety risks. While traditional fault diagnosis methods can detect typical faults, they struggle to capture texture features. Texture features directly reflect the physical degradation process of PV modules and are significant indicators of faults. However, existing deep learning methods, although effective at extracting image features, do not focus on this crucial aspect, leading to insufficient sensitivity when classifying complex fault patterns (such as shadowing and aging overlap). This results in unsatisfactory classification performance. To address this issue, this paper proposes a texture-feature-enhanced fault classification method for PV modules. First, texture features from fault images were extracted using the gray-level co-occurrence matrix (GLCM). Then, a pilot study was conducted to verify the close relationship between texture features and PV faults. A feature fusion module was designed to combine these texture features with global features extracted by the ResNet50 network, enhancing the model. Ultimately, the texture-enhanced model achieved accurate fault classification, significantly improving the model's sensitivity to fault textures. The proposed model was evaluated on a test dataset and compared with four other classical models. Experimental results showed that the proposed model outperforms existing models in both accuracy and recall, further validating the key role of texture features in model performance and providing a new reference for research in PV module fault classification. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 190785277
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  Label: Title
  Group: Ti
  Data: An Enhanced Fault Classification Method for Photovoltaic Modules Using Texture Features.
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  Data: <searchLink fieldCode="AR" term="%22Meng%2C+Qiang%22">Meng, Qiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Shenji%22">Zhu, Shenji</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Dou%22">Wang, Dou</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Zeying%22">Fan, Zeying</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Chenghang%22">Zheng, Chenghang</searchLink><relatesTo>1,4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Xiang%22">Gao, Xiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xgao1@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cen%2C+Kefa%22">Cen, Kefa</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jan2026, Vol. 19 Issue 1, p131. 16p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Photovoltaic+power+generation%22">Photovoltaic power generation</searchLink><br />*<searchLink fieldCode="DE" term="%22Surface+texture%22">Surface texture</searchLink><br />*<searchLink fieldCode="DE" term="%22Surface+properties%22">Surface properties</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+production%22">Electric power production</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In the field of photovoltaic (PV) power generation, PV systems are prone to faults such as cells and cracks due to prolonged operation in complex environments, which reduce power generation efficiency and pose safety risks. While traditional fault diagnosis methods can detect typical faults, they struggle to capture texture features. Texture features directly reflect the physical degradation process of PV modules and are significant indicators of faults. However, existing deep learning methods, although effective at extracting image features, do not focus on this crucial aspect, leading to insufficient sensitivity when classifying complex fault patterns (such as shadowing and aging overlap). This results in unsatisfactory classification performance. To address this issue, this paper proposes a texture-feature-enhanced fault classification method for PV modules. First, texture features from fault images were extracted using the gray-level co-occurrence matrix (GLCM). Then, a pilot study was conducted to verify the close relationship between texture features and PV faults. A feature fusion module was designed to combine these texture features with global features extracted by the ResNet50 network, enhancing the model. Ultimately, the texture-enhanced model achieved accurate fault classification, significantly improving the model's sensitivity to fault textures. The proposed model was evaluated on a test dataset and compared with four other classical models. Experimental results showed that the proposed model outperforms existing models in both accuracy and recall, further validating the key role of texture features in model performance and providing a new reference for research in PV module fault classification. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19010131
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 131
    Subjects:
      – SubjectFull: Photovoltaic power generation
        Type: general
      – SubjectFull: Surface texture
        Type: general
      – SubjectFull: Surface properties
        Type: general
      – SubjectFull: Statistical accuracy
        Type: general
      – SubjectFull: Electric power production
        Type: general
      – SubjectFull: Fault diagnosis
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Image processing
        Type: general
    Titles:
      – TitleFull: An Enhanced Fault Classification Method for Photovoltaic Modules Using Texture Features.
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            NameFull: Meng, Qiang
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            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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              Value: 19
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            – TitleFull: Energies (19961073)
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