FlashDetR: A deep learning pipeline for early detection and time estimation of flashover in high-voltage insulators using infrared videos
Bibliographic record
Abstract
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. • End-to-end DL model for early flashover prediction and precise time-to-flashover. • IR video dataset captured in controlled conditions, showing full DBA progression. • High-accuracy models for early flashover detection and low MAE for time-to-flashover prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".