Prototype of a Negative-Sequence Turn-to-Turn Fault Detection Scheme for Transformers
Bibliographic record
Abstract
Digital relays are capable of computing the negative-sequence current on both primary and secondary sides of the transformer along with the phase difference between these two negative-sequence currents. By using both phase and magnitude information, negative-sequence current could be used to detect minor turn-to-turn faults involving 3% of the transformer's windings or more. Turn-to-turn faults may still occur even if no current is flowing on one side of the transformer, such as during energization. With no current flowing in the secondary windings of the transformer, negative-sequence current-based algorithms become insensitive. This paper introduces a relay prototype, using both negative-sequence current and negative-sequence voltage, which retains its sensitivity during energization. The relay's performance for several commonly encountered system scenarios, such as overexcitation, current-transformer saturation, nonzero fault resistance, transformer energization, and external faults were also examined. The experimental results presented in this paper indicate that the algorithm proposed in this paper is faster and more sensitive than restrained current differential protection and is capable of detecting turn-to-turn faults occurring during transformer energization.
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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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".