Effect of Moisture and Aging of Kraft Paper Immersed in Mineral Oil and Synthetic Ester on Bubbling Inception Temperature in Power Transformers
Bibliographic record
Abstract
Bubbling Inception Temperature (BIT) is a critical metric that indicates the temperature at which gas bubbles form due to cellulose decomposition in a paper–oil insulation system. It serves as a key indicator of the thermal stability of transformer insulation, offering valuable insights into its performance under elevated temperatures. Building on findings from a companion study that examined the BIT of Kraft paper (KP), thermally upgraded Kraft paper (TUK), and aramid paper in mineral oil, this research expands the analysis to assess the impact of moisture, aging, and alternative dielectric fluids. Using the same customized experimental setup featuring precise dynamic load control, real-time bubble detection, and continuous monitoring of moisture and temperature, this study evaluates BIT across four distinct oil–paper aging stages: new (0 h) and 2 weeks, 4 weeks, and 6 weeks of accelerated thermal aging. This approach enables a comparative analysis of BIT in various paper–oil systems, focusing on both mineral oil and synthetic esters, as well as the influence of different moisture levels in the paper insulation. The results show that BIT decreases with aging, indicating reduced thermal stability. Furthermore, KP impregnated with synthetic ester exhibits a higher BIT than when impregnated with mineral oil, suggesting that synthetic esters may offer better resistance to bubble formation under thermal stress. Based on these results, empirical BIT models were developed as a function of degree of polymerization (DP) and water content in paper (WCP). This study further demonstrates how these models can be applied to quantify safety margins under emergency overloading conditions, providing a practical tool for operational decision-making in transformer thermal risk management.
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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".