FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos
<p>Flashover in high-voltage insulators poses a significant risk to power system reliability, potentially leading to outages and safety hazards. This study introduces an innovative deep learning-based approach for early prediction of flashover events and time-to-flashover estimation by analyzi...
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2025
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| _version_ | 1864513531997585408 |
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| author | Najmath Ottakath (17430912) |
| author2 | Abdullah Lutfi (22804337) Ali Hamdi (13432680) Khaled Shaban (20074425) Ayman El-Hag (20055087) |
| author2_role | author author author author |
| author_facet | Najmath Ottakath (17430912) Abdullah Lutfi (22804337) Ali Hamdi (13432680) Khaled Shaban (20074425) Ayman El-Hag (20055087) |
| author_role | author |
| dc.creator.none.fl_str_mv | Najmath Ottakath (17430912) Abdullah Lutfi (22804337) Ali Hamdi (13432680) Khaled Shaban (20074425) Ayman El-Hag (20055087) |
| dc.date.none.fl_str_mv | 2025-11-30T18:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2025.113256 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/FlashDetR_A_deep_learning_pipeline_for_early_detection_and_time_estimation_of_flashover_in_high-voltage_insulators_using_infrared_videos/30820190 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Dry band arcing Flashover Early prediction Transformer model Deep learning Time-to-flashover |
| dc.title.none.fl_str_mv | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Flashover in high-voltage insulators poses a significant risk to power system reliability, potentially leading to outages and safety hazards. This study introduces an innovative deep learning-based approach for early prediction of flashover events and time-to-flashover estimation by analyzing infrared videos of dry band arcing, a known precursor to flashover. In this work, we propose a pipeline named Flashover Detector and Time Estimator, which integrates a transformer-based model to accurately predict flashover occurrences, while a Three Dimensional Convolutional Neural Network-based model estimates the time to flashover. Flashover Detector and Time Estimator progressively samples video frames at multiple scales, enhancing prediction accuracy. Experimental results demonstrate that the models achieve up to 88.73% accuracy in predicting flashover events and a mean absolute error of 3.41 in time-to-flashover estimation. These findings substantially improve the ability to implement preventive measures. Flashover Detector and Time Estimator thus represents a significant advancement in proactively managing power system reliability, with demonstrated effectiveness and real-time application potential.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.113256" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.113256</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_47a2185dd43782fd2fc1e73a784849a4 |
| identifier_str_mv | 10.1016/j.engappai.2025.113256 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30820190 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videosNajmath Ottakath (17430912)Abdullah Lutfi (22804337)Ali Hamdi (13432680)Khaled Shaban (20074425)Ayman El-Hag (20055087)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceMachine learningDry band arcingFlashoverEarly predictionTransformer modelDeep learningTime-to-flashover<p>Flashover in high-voltage insulators poses a significant risk to power system reliability, potentially leading to outages and safety hazards. This study introduces an innovative deep learning-based approach for early prediction of flashover events and time-to-flashover estimation by analyzing infrared videos of dry band arcing, a known precursor to flashover. In this work, we propose a pipeline named Flashover Detector and Time Estimator, which integrates a transformer-based model to accurately predict flashover occurrences, while a Three Dimensional Convolutional Neural Network-based model estimates the time to flashover. Flashover Detector and Time Estimator progressively samples video frames at multiple scales, enhancing prediction accuracy. Experimental results demonstrate that the models achieve up to 88.73% accuracy in predicting flashover events and a mean absolute error of 3.41 in time-to-flashover estimation. These findings substantially improve the ability to implement preventive measures. Flashover Detector and Time Estimator thus represents a significant advancement in proactively managing power system reliability, with demonstrated effectiveness and real-time application potential.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.113256" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.113256</a></p>2025-11-30T18:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2025.113256https://figshare.com/articles/journal_contribution/FlashDetR_A_deep_learning_pipeline_for_early_detection_and_time_estimation_of_flashover_in_high-voltage_insulators_using_infrared_videos/30820190CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308201902025-11-30T18:00:00Z |
| spellingShingle | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos Najmath Ottakath (17430912) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Dry band arcing Flashover Early prediction Transformer model Deep learning Time-to-flashover |
| status_str | publishedVersion |
| title | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| title_full | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| title_fullStr | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| title_full_unstemmed | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| title_short | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| title_sort | FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Dry band arcing Flashover Early prediction Transformer model Deep learning Time-to-flashover |