Bubbling Inception Temperature in Power Transformers—Part 1: Comparative Study of Kraft Paper, Thermally Upgraded Kraft Paper, and Aramid Paper With Mineral Oil
Bibliographic record
Abstract
The bubbling inception temperature (BIT) of insulating materials used in transformers is critical for their performance and lifespan. This study, which represents the first part of a two-part series, provides a comparative analysis of the BIT for kraft paper, thermally upgraded kraft paper (TUK), and aramid paper impregnated with mineral oil. A customized experimental setup was used to measure the BIT under controlled laboratory conditions. The uniqueness of the setup lies in its precise control of dynamic load conditions via an autotransformer, real-time bubble detection, continuous moisture in oil and temperature monitoring using sensors, the use of capacitive measurement to assess moisture content in paper, and the flexibility to test different oils and insulation materials. This combination enables accurate analysis of bubble formation in oil-paper insulation systems under realistic conditions. Results show that TUK paper has the highest BIT, followed by kraft and aramid papers. Additionally, the study introduces new empirical equations for predicting BIT based on water content for each paper type, notably filling a gap for aramid paper. These equations are valuable for practical engineering applications. The research underscores the importance of moisture control in determining BIT and suggests future studies focused on standardizing methodologies and exploring different dielectric fluids. The findings contribute to improving the design, maintenance, and reliability of transformer insulation systems. Part 2 of this study further explores the long-term effects of thermal aging and alternative dielectric fluids on BIT.
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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 | 0.001 |
| 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) | 0.001 | 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 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".