Monitoring leakage current of ice-covered station post insulators using artificial neural networks
Bibliographic record
Abstract
This paper presents the analysis of leakage current evolution of an ice-covered station post insulator during a melting period using artificial neural network (ANN) models. The tests, carried out under wet-grown ice regime for different experimental conditions, showed that the permanent establishment of white arcs, identified as ¿permanent regime¿ led to flashover in the large majority of the cases,. Based on these observations, the development of a monitoring methodology aimed at forewarning the approach of the leading white arc during melting periods is proposed. The monitoring methodology uses different ANNs in order to predict the appearance of the white arc based on the identification, classification and analysis of the occurrence frequency of electric discharges. The results show that the ANN monitoring model developed is able to predict the onset of permanent regime under various experimental conditions. Hence, it was found that the delay between the permanent regime onset prediction delivered by the ANN model and its realization is 9 minutes on average. These results confirm that the proposed ANN model could be used as part of a monitoring system for post insulators during icing events for protection against potential flashover hazards.
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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 | 0.001 | 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 | 0.001 |
| 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".