Phase-Based Digital Protection for Arc Flash Faults
Bibliographic record
Abstract
This paper presents the real-time implementation and experimental performance evaluation of the phase-based digital protection against arc flash faults. The tested digital protection is based on extracting the high frequency components from fault currents triggered by an arc flash fault. The desired high frequency components are extracted using a filter bank that is composed of five exponentially modulated Kaiser window-based high-pass filters (HPFs). The structure of the used filter bank is selected to ensure extracting high frequency components with nonstationary phases, which represent a unique signature of arc flash faults. Such a signature allows detecting and identifying arc flash faults, as well as initiating responses against such events. The performance of the phase-based digital protection is experimentally evaluated for a laboratory 3φ system that supplies linear, nonlinear, and dynamic loads. Test results demonstrate fast, accurate, and reliable detection, identification, and response to arc flash faults. In addition, test results show that the phase-based digital protection has minor sensitivity to the type of arc flash fault or supplied loads.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".