Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks
Every year, each solar panel suffers an efficiency loss of 0.5% to 1%. This degradation of solar panels arises due to environmental and electrical faults. A timely and accurate diagnosis of environmental faults reduces the damage caused by faults on the panel. In recent years, deep learning precisel...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/2872925 |
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| author | Tamilselvi Selvaraj Ramasubbu Rengaraj GiriRajanbabu Venkatakrishnan SoundhariyaGanesan Soundararajan Karuppiah Natarajan PraveenKumar Balachandran PrinceWinston David Shitharth Selvarajan |
| author_facet | Tamilselvi Selvaraj Ramasubbu Rengaraj GiriRajanbabu Venkatakrishnan SoundhariyaGanesan Soundararajan Karuppiah Natarajan PraveenKumar Balachandran PrinceWinston David Shitharth Selvarajan |
| author_sort | Tamilselvi Selvaraj |
| collection | DOAJ |
| description | Every year, each solar panel suffers an efficiency loss of 0.5% to 1%. This degradation of solar panels arises due to environmental and electrical faults. A timely and accurate diagnosis of environmental faults reduces the damage caused by faults on the panel. In recent years, deep learning precisely convolutional neural networks have achieved wonderful results in many applications. This work is focused on finely tuning pretrained models of convolutional neural networks, especially AlexNet, GoogleNet, and SqueezeNet. Based on the performance metrics, SqueezeNet is used for training thermal images of solar panels and for the classification of environmental faults. The results obtained show that SqueezeNet has a significant testing accuracy of 99.74% and F1 score of 0.9818, which make the model successful in identifying environmental faults in solar panels and help users to protect the panels. |
| format | Article |
| id | doaj-art-453f3ca11c9a4ac88c6f70ee63d5f1db |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-453f3ca11c9a4ac88c6f70ee63d5f1db2025-02-03T01:20:00ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/2872925Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural NetworksTamilselvi Selvaraj0Ramasubbu Rengaraj1GiriRajanbabu Venkatakrishnan2SoundhariyaGanesan Soundararajan3Karuppiah Natarajan4PraveenKumar Balachandran5PrinceWinston David6Shitharth Selvarajan7Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Computer Science and EngineeringEvery year, each solar panel suffers an efficiency loss of 0.5% to 1%. This degradation of solar panels arises due to environmental and electrical faults. A timely and accurate diagnosis of environmental faults reduces the damage caused by faults on the panel. In recent years, deep learning precisely convolutional neural networks have achieved wonderful results in many applications. This work is focused on finely tuning pretrained models of convolutional neural networks, especially AlexNet, GoogleNet, and SqueezeNet. Based on the performance metrics, SqueezeNet is used for training thermal images of solar panels and for the classification of environmental faults. The results obtained show that SqueezeNet has a significant testing accuracy of 99.74% and F1 score of 0.9818, which make the model successful in identifying environmental faults in solar panels and help users to protect the panels.http://dx.doi.org/10.1155/2022/2872925 |
| spellingShingle | Tamilselvi Selvaraj Ramasubbu Rengaraj GiriRajanbabu Venkatakrishnan SoundhariyaGanesan Soundararajan Karuppiah Natarajan PraveenKumar Balachandran PrinceWinston David Shitharth Selvarajan Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks International Transactions on Electrical Energy Systems |
| title | Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks |
| title_full | Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks |
| title_fullStr | Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks |
| title_full_unstemmed | Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks |
| title_short | Environmental Fault Diagnosis of Solar Panels Using Solar Thermal Images in Multiple Convolutional Neural Networks |
| title_sort | environmental fault diagnosis of solar panels using solar thermal images in multiple convolutional neural networks |
| url | http://dx.doi.org/10.1155/2022/2872925 |
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