Realization of transformer winding network from sweep frequency response data
Bibliographic record
Abstract
Sweep Frequency Response analysis (SFRA) is a widely used technique for condition assessment of power transformers and reactors. With SFRA, it is possible to analyze the integrity of transformer without prior dismantling. Changes in geometric configuration change the impedance of network which in turn changes the transfer function. Changes in transfer function will reveal a wide range of failure modes. SFRA allows the detection of changes in transfer function of individual windings within transformers and reactors and indicate movement or distortion in core and windings of the transformer. The RLC network can be identified by its frequency dependent transfer function. In order to create a framework to facilitate assisted interpretation algorithms, SFRA curves are parameterized. The simplest and most useful mechanism is the reduction of the curves to pole-zero representation. SFRA traces are characterized by having many undulations and extending over a wide dynamic range on both axes. This can make accurate pole-zero representation a challenge. In this paper an attempt has been made to realize the electrical network from the SFRA response data of a high voltage reactor.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".