A negative-sequence based method for fault passage identification
Bibliographic record
Abstract
Reliable fault indication is crucial in any distribution feeder management system. A distribution line protective device can use different algorithms to detect whether or not it is in the fault path. When the downstream fault involves all three phases, detection is typically easy to achieve and phase elements are sufficient. Single line to ground faults cannot always be treated in the same manner, as ground sources are prevalent throughout the grid. In these cases, directionality (achieved by voltage polarization) is required. However, the requirement of both current and voltage measurements could render the solution cost prohibitive. If voltage is not available, detection may fail. Hence, there is the need for a simple but reliable ground fault indicator which is based on current measurements solely. This paper proposes the use of negative-sequence current to achieve this purpose. It is inspired by the fact that there is only one source of negative-sequence currents in a radial system. The proposed settings were implemented in large scale in the city of Fort McMurray throughout ATCO Electric's DSCADA devices. Such inputs are used by the feeder automation system to reconfigure the system automatically in case of permanent outages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".