Injection Current into the Power Transformer as an Internal Fault Phenomena for Measuring the Differential Relay Performance
Bibliographic record
Abstract
In this paper explains the classical aspects in measure the differential relay performance. The classical aspects use the simulation through inrush current into the power transformer under three conditions. The first condition is giving the normal phenomenon as phenomenon of loading, the second condition is giving the fault phenomenon for outside of the protection zone, and the third condition is giving the fault phenomenon for inside of the protection zone. The result of measurement during loading condition, the differential relay not operate, because relay is set with value of 130% of the largest current between the three phases, so that the adjustable current is 4.45 mA. The results of current measurement to the differential relay when the fault condition is provided outside of the protection area, the current value to the differential relay should of zero mA and the differential relay not operate. The result of measurement of the current flow to the differential relay when giving the fault phenomenon inside the protection zone is obtained flow to relay at the phase-R is 127.7 mA, phase-S is 123.9 mA, and phase-T equal to 123.8 mA, while the result of calculation of the current flow to the differential relay of each phase of 152.61 mA, and the differential relay operate. After giving all of three conditions, the differential relay performance is known.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".