A New Technique to Detect Faults in De-Energized Distribution Feeders—Part II: Symmetrical Fault Detection
Bibliographic record
Abstract
To ensure safe re-energizing of an overhead distribution feeder after it is de-energized for an extended period, a novel fault detection technique by controlling a thyristor-based device is proposed in a companion paper. The device connected in parallel with a breaker or recloser can inject electrical pulses with adjustable strength for the downstream fault detection in a de-energized system. The proposed method can effectively detect different kinds of asymmetrical faults based on the unbalanced fault currents. However, the unbalanced current-based fault detection scheme is not effective for three-phase symmetrical faults detection. Furthermore, a stalled motor or a shunt-connected capacitor bank in the downstream may also behaves like a short-circuit. Therefore, a fault detection algorithm based on the analysis of the harmonic impedance of the de-energized system is developed in this paper. This method is very effective for the symmetrical fault detection and for distinguishing a stalled motor and capacitor bank from a fault. Extensive lab test results are provided in the paper to verify the effectiveness of the proposed method.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".