The State-of-the-Art Methods for Digital Detection and Identification of Arcing Current Faults
Bibliographic record
Abstract
This paper reviews approaches used to detect and identify arcing currents, including arcing current faults. The reviewed approaches are categorized as the time-domain, frequency-domain, and time-frequency approaches. The time-domain approach extracts shoulders (zero values of the current around zero crossing points), spikes and jumps, abnormal magnitudes (lower or higher than normal), and high rate of change of the current. The frequency-domain approach extracts the high-frequency components, harmonic components, sub-harmonic components, and cross-correlation indicator. The time-frequency approach extracts high-frequency sub-bands that contain non-stationary frequency components, which may have non-stationary phases. The three approaches are implemented to test their accuracy, computational requirements, and sensitivity to system parameters. These tests are performed by processing of currents that are collected for normal and dynamic conditions, conventional faults, and currents with high or low arcing components. Test results provide a performance comparison for the tested approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".