Bubbling Phenomena in Liquid-Filled Transformers: Background and Assessment
Bibliographic record
Abstract
The degradation of the insulation system in liquid-filled power transformers is a serious concern for electric power utilities. The insulation system’s ageing is accelerated by moisture, acids, oxidation products, and other decay particles (soluble and colloidal). The presence of these ageing by-products is detrimental to the insulation system and may further lead to premature ageing and serious consequences. The ageing mechanisms of oil-paper insulation are complex, highly interrelated, and strongly temperature-dependent. The operating temperature of the transformer insulating system has a direct relationship with the loading profile. The major aspect that is witnessed with the fluctuating temperatures is moisture migration and subsequent bubble evolution. In other words, gas bubbles evolve from the release of water vapor from the cellulosic insulation wrapped around the transformer windings. The models presented in the existing standards, such as the IEC Std. 60076-7:2018 and the IEEE Std. C57.91:2011, are mainly based on the insulation temperature, which acts as a key parameter. Several studies have investigated the moisture dynamics and bubbling phenomenon as a function of the water content in the paper and the state of the insulation system. Some studies have reported different prototypes for the estimation of the bubble inception temperatures under selected conditions. However, there are various attributes of the insulation system that are to be considered, especially when expanding the models for the alternative liquids. This paper reviews various evaluation models reported in the literature that help understand the bubbling phenomenon in transformer insulation. The discussions also keep us in the loop on the estimation of bubbling behavior in alternative dielectric liquids and key attributable factors for use in transformers. In addition, useful tutorial elements focusing on the bubbling issue in transformers as well as some critical analyses are addressed for future research on this topic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".