Methanol Marker for the Detection of Insulating Paper Degradation in Transformer Insulating Oil
Bibliographic record
Abstract
This manuscript presents a comprehensive literature review with the aim to provide readers a reference document with up-to-date information on the field of methanol use as a chemical marker. It has been a little more than a decade since methanol was first introduced as a marker for assessing solid insulation condition in power transformers. It all started when methanol was identified in the laboratory during thermal ageing tests carried out with oil-immersed insulating papers and was subsequently also identified in transformer field samples. The first publication on the subject was released in 2007 by our research group. This review covers the methanol fundamentals such as the analytical methods for its determination in transformer oil, which is generally performed by headspace gas chromatography with mass spectrometry or flame ionization as a detector. Current standardization efforts for its determination include ASTM working group 30948 and IEC TC10. Kinetic studies have confirmed the relationship between methanol generation, the number of broken 1,4-β-glycosidic bonds of cellulose and changes in mechanical properties. Laboratory tests have confirmed its stability at different accelerated ageing temperatures. Several utilities have identified methanol during field measurements, case studies on power and some distribution transformers are presented, as well as transformer postmortem investigations. These field-testing results demonstrate its utility in monitoring cellulosic insulation degradation. Recently, a model of methanol interpretation has become available that allows for evaluation of the average degree of polymerization of core type transformer cellulose winding. Methanol has a role as an indicator of cellulosic solid insulation ageing in transformer mineral oil, and it is expected that in the future it will be in routine use by utilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".