Effect of thermal transient on the polarization and depolarization current measurements
Bibliographic record
Abstract
In the last two decades, some new methods, based on the characterization of the dielectric response of transformer insulation in both time and frequency domains are being used by power utilities for assessment of the condition of power equipments. From fields and laboratory investigations, these techniques were found to be highly operating conditions dependant. During normal operating conditions, the temperature inside a transformer is much higher than ambient, depending upon the operating condition. Because field measurements last hours after switching off the transformer, the final temperature may be much lower than the initial. Thus at onsite measurements the water migration is commonly running, the transformer is in a non equilibrium state. This transient can lead to mistaken interpretation of insulation condition. In the current research work, a systematic investigation on the influence of thermal transients on the results of polarization and depolarization current measurements is presented. A series of experiments have been performed under controlled temperature conditions on an oil impregnated bushing model. Precautions that can be taken to minimize thermal transient effects are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".