A new algorithm to discriminate internal fault current and inrush current utilizing feature of fundamental current
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel method to identify inrush current and internal fault current using the two-instantaneous-value-product algorithm to extract the variation feature of the fundamental current amplitude. First, the two-instantaneous-value-product algorithm is developed, and different variation trends of the fundamental current amplitude for inrush current and internal fault current are analyzed. Then, according to the descending features of the fundamental current amplitude, the inrush current and internal fault current can be distinguished from each other. A total of 216 experimental measurements have been tested on an YNd11 connected transformer. Dynamic testing results indicate that this method is able to clear internal faults, even light ones, within a cycle and is not affected by the current transformer saturation. Moreover, compared with the second harmonic restraint principle and the waveform comparison principle, the proposed algorithm has better performance. The computational simplicity of the proposed scheme enables its implementation in real-time applications with low-cost microprocessors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it