Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm
As the demand for renewable energy sources increases, the vulnerability of solar panels to lightning strikes becomes a critical concern. This research explores the correlation between lightning-induced voltage fluctuations and the resultant damage intensity on solar panels. The study adopts a system...
| Main Authors: | , , , , , |
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| Format: | Article |
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Taylor and Francis
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116338/ |
| _version_ | 1848866978981216256 |
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| author | Chandrasegaran, Ormiila Mustapha, Faizal Abdullah, Mohd Na’im Anwar, Murniwati Mustapha, Mazli Adzis, Zuraimy |
| author_facet | Chandrasegaran, Ormiila Mustapha, Faizal Abdullah, Mohd Na’im Anwar, Murniwati Mustapha, Mazli Adzis, Zuraimy |
| author_sort | Chandrasegaran, Ormiila |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | As the demand for renewable energy sources increases, the vulnerability of solar panels to lightning strikes becomes a critical concern. This research explores the correlation between lightning-induced voltage fluctuations and the resultant damage intensity on solar panels. The study adopts a systematic approach, first investigating the correlation between lightning-induced voltage assessment using 30kV, 60kV and 90 kV impulse voltage with multi-stage Marx impulse generator and the damage intensity on monocrystalline and polycrystalline solar panels. A portable active infrared thermography equipment with tCam-Mini was employed to conduct this research with wireless streaming. Furthermore, the study integrates Convolutional Neural Network (CNN)-based image classification techniques to enhance the efficiency of damage assessment. The results highlight the potential of neural networks in improving the accuracy and speed of image classification for damaged and undamaged samples. The findings contribute valuable insights into enhancing the resilience of solar panel systems against lightning strikes, ultimately advancing the reliability and sustainability of solar energy infrastructure. A new CNN model was developed to classify the images obtained from thermography with 90.21% accuracy for greyscale and 85.31% accuracy on thermal images. © 2024 Informa UK Limited, trading as Taylor & Francis Group. |
| first_indexed | 2025-11-15T14:29:12Z |
| format | Article |
| id | upm-116338 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:29:12Z |
| publishDate | 2024 |
| publisher | Taylor and Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1163382025-03-25T03:12:49Z http://psasir.upm.edu.my/id/eprint/116338/ Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm Chandrasegaran, Ormiila Mustapha, Faizal Abdullah, Mohd Na’im Anwar, Murniwati Mustapha, Mazli Adzis, Zuraimy As the demand for renewable energy sources increases, the vulnerability of solar panels to lightning strikes becomes a critical concern. This research explores the correlation between lightning-induced voltage fluctuations and the resultant damage intensity on solar panels. The study adopts a systematic approach, first investigating the correlation between lightning-induced voltage assessment using 30kV, 60kV and 90 kV impulse voltage with multi-stage Marx impulse generator and the damage intensity on monocrystalline and polycrystalline solar panels. A portable active infrared thermography equipment with tCam-Mini was employed to conduct this research with wireless streaming. Furthermore, the study integrates Convolutional Neural Network (CNN)-based image classification techniques to enhance the efficiency of damage assessment. The results highlight the potential of neural networks in improving the accuracy and speed of image classification for damaged and undamaged samples. The findings contribute valuable insights into enhancing the resilience of solar panel systems against lightning strikes, ultimately advancing the reliability and sustainability of solar energy infrastructure. A new CNN model was developed to classify the images obtained from thermography with 90.21% accuracy for greyscale and 85.31% accuracy on thermal images. © 2024 Informa UK Limited, trading as Taylor & Francis Group. Taylor and Francis 2024 Article PeerReviewed Chandrasegaran, Ormiila and Mustapha, Faizal and Abdullah, Mohd Na’im and Anwar, Murniwati and Mustapha, Mazli and Adzis, Zuraimy (2024) Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm. Nondestructive Testing and Evaluation. ISSN 1058-9759; eISSN: 1477-2671 https://www.tandfonline.com/doi/full/10.1080/10589759.2024.2410386 10.1080/10589759.2024.2410386 |
| spellingShingle | Chandrasegaran, Ormiila Mustapha, Faizal Abdullah, Mohd Na’im Anwar, Murniwati Mustapha, Mazli Adzis, Zuraimy Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm |
| title | Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm |
| title_full | Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm |
| title_fullStr | Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm |
| title_full_unstemmed | Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm |
| title_short | Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm |
| title_sort | lightning damage assessment on solar panels using in-house portable infrared thermography camera with convolutional neural network (cnn) algorithm |
| url | http://psasir.upm.edu.my/id/eprint/116338/ http://psasir.upm.edu.my/id/eprint/116338/ http://psasir.upm.edu.my/id/eprint/116338/ |