An experimental implementation of dq axis wavelet packet transform hybrid technique for 3Ø power transformer protection
Bibliographic record
Abstract
A successful development and implementation of dq axis components and wavelet packet transform (WPT) based hybrid technique for power transformer protection is introduced in this paper. In this approach, the high frequency sub-band contents of dq axis components of the differential current is extracted using the WPT. This characterization helps to provide enough information to efficiently detect and discriminate internal faults from inrush currents in power transformers. This hybrid method provides accurate information with only one level of the Wavelet Packet Transform (WPT) of the dq components of the differential current for power transformer protection. A real-time experiment is carried out for different normal and abnormal operating conditions such as inrush and internal faults for different cases of loading to test the efficacy of the proposed algorithm. The experimental results show fast, accurate, and reliable responses to all different types of disturbances that may occur in power transformers.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".