Mingzheng Sun, Hao Li. Infrared Image Fault Detection of Photovoltaic Modules Based on Residual Photovoltaic Network[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2412005
- Laser & Optoelectronics Progress
- Vol. 60, Issue 24, 2412005 (2023)
Fig. 1. Model of ResPNet
Fig. 2. Model of Residual block
Fig. 3. Model of Respblock
Fig. 4. Model of Gblock
Fig. 5. Ensemble neural network framework for prediction of photovoltaic modules
Fig. 6. Training loss of each model
Fig. 7. Validation accuracy of each model
Fig. 8. Feature layer visualization information of underlying feature information enhancement module. (a) Feature maps information of ResNet-50; (b) feature maps information of ResPNet[D]
Fig. 9. Feature layer visualization information of Respblock module. (a) Feature maps information of ResPNet[D]; (b) feature maps information of ResPNet[P]
Fig. 10. Feature layer visualization information of Gblock module. (a) Feature maps information of ResPNet[P]; (b) feature maps information of ResPNet[G]
Fig. 11. Test results of different photovoltaic module fault detection models. (a) ResNet; (b) ResPNet
Fig. 12. Test results of cascaded photovoltaic module fault detection
|
Table 1. Description of photovoltaic module and dataset partitioning
|
Table 2. Type of photovoltaic module images
|
Table 3. Ablation experiment results of infrared image classification accuracy of photovoltaic modules
|
Table 4. Model accuracy of infrared image classification in photovoltaic modules
|
Table 5. Integrated models for infrared image classification accuracy of photovoltaic module
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20