An Experimental Implementation of the <inline-formula> <tex-math notation="TeX">$dq$</tex-math></inline-formula>-Axis Wavelet Packet Transform Hybrid Technique for Three-Phase Power Transformer Protection
Bibliographic record
Abstract
A successful development and implementation of the dq-axis components and the wavelet packet transform (WPT)-based hybrid technique for power transformer protection is introduced in this paper. In this approach, the high-frequency subband contents of the 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 WPT of the dq-axis 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 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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".