Digital Protection Against Arcing Current Faults on the Secondary Side of a Three Phase Power Transformer
Bibliographic record
Abstract
This paper discusses challenges in detecting, identifying, and responding to low voltage (<1000V) arcing current faults (ACFs), which can occur on the secondary side of 3$\phi$ medium voltage-to-low voltage power transformers. Secondary side ACFs trigger currents with magnitudes lower than those triggered by conventional faults, thus reducing the ability of medium voltage (MV) side protective devices to detect and respond to such faults. In many cases, the reduced ability to detect and respond to LV side ACFs prolongs the duration of these ACFs, and leads to a significant increase in the incident energy (may exceed acceptable limits). This paper presents an analysis of MV side currents to extract signature information to detect and identify LV side ACFs. The desired LV side ACF signature is extracted as high frequency components that have non-stationary phases. Such frequency components can be extracted using a multi-channel filter bank composed of digital high pass finite impulse response filters, which have linear phase responses. The non-stationary phase approach is tested several transient events including LV side ACFs. Performance results reveal accurate and reliable detection, identification, and response to LV side ACFs with negligible sensitivity to loading level and/or ACF type (series or parallel).
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".