Multi-resolution analysis based data compression for power transformer protection
Bibliographic record
Abstract
In this paper, a multi-resolution hybrid technique based data compression for protection of power transformer is presented. This hybrid technique is established by combining the d-q axis transformation with the wavelet packet transform (WPT) to provide the proposed (dqWPT) hybrid protection technique for power transformer. In this technique, signal decomposition and reconstruction are carried out for the collected fault and inrush current signals for different types of mother wavelets at a certain number of levels. The number of levels has to be kept the minimum for optimal use. Minimum description length (MDL) method is used to select the best mother wavelet, and the optimal number of decomposition levels required for the power transformer protection using the proposed technique. For this analysis, the Daubechies db4 is selected at one level of resolution is utilized to implement the proposed technique. The proposed technique has shown good results with the selected mother wavelet for different disturbances at the power transformer.
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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 | 0.000 |
| 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".