A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images

Chiwu Bu, Tao Liu, Tao Wang, Hai Zhang and Stefano Sfarra ()
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Chiwu Bu: School of Light Industry, Harbin University of Commerce, Harbin 150028, China
Tao Liu: School of Light Industry, Harbin University of Commerce, Harbin 150028, China
Tao Wang: Intelligent Manufacturing Engineering Department, Zibo Technician College, Zibo 255000, China
Hai Zhang: Centre for Composite Materials and Structures (CCMS), Harbin Institute of Technology, Harbin 150001, China
Stefano Sfarra: Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale E. Pontieri 1, Monteluco di Roio, 67100 L’Aquila, AQ, Italy

Energies, 2023, vol. 16, issue 9, 1-13

Abstract: Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set. In order to overcome the problem of the original dataset’s scarcity, an offline data augmentation method is adopted to improve the generalization ability of the network. During the experiment, the effectiveness of the proposed model is evaluated by quantifying the obtained results with four deep learning models through evaluation indicators. The fault classification accuracy of the CNN model proposed here has been drawn by the experiment and reaches 97.42%, and it is superior to that of the models of AlexNet, VGG 16, ResNet 18 and existing models. In addition, the proposed model has faster calculation, prediction speed and the highest accuracy. This method can well-identify and classify PV cell faults and has high application potential in automatic fault identification and classification.

Keywords: PV cell faults; automatic fault classification; CNN; deep learning; thermography (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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