Fast and Reliable Method for Identifying Fault Type and Faulted Phases Using Band Limited Transient Currents
Bibliographic record
Abstract
A method for fault type discrimination and faulted phase identification using instantaneous local current measurements is proposed. Seven current components are obtained from the measured three-phase instantaneous currents through a transformation and band passed filtered to remove the fundamental components and high frequency content. Upon detecting a fault, nine fault discrimination indices are computed taking different ratios of the maximum rates of change of these filtered current components. The indices are used to (i) separate ground faults from the phase-to-phase and three-phase faults, (ii) discriminate phase-to-ground from phase-to-phase-to-ground faults, (iii) differentiate phase-to-phase faults from the three-phase faults, and (iv) identify the faulted phases. As the indices are ratios, decisions are less affected by the fault resistance. The algorithm was validated using a power system simulated in PSCAD/EMTDC. Extensive testing involving more than 10,000 fault scenarios showed that fault type and faulted phases can be successfully identified. The algorithm found some difficulty in identifying the phases involved in phase-to-phase-to-ground faults at certain fault inception angles, and one of the thresholds can be affected by the transposition scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.002 |
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".