Power transformer condition monitoring by 2FAL content and CO2/CO ratio – A fuzzy logic approac
Bibliographic record
Abstract
The condition of the solid insulation paper within transformers can be directly determined from the degree of polymerization (DP) measured from samples. Since this is difficult for in service units, many studies have been performed in regard with indirectly measuring the degradation of paper insulation by chemical markers for several decades. The 2-FAL concentration is being used decades ago. In this contribution, condition monitoring history and experience with transformer fleets are reported for a service aging of 75 years. 37 oil-filled transformers (146.7 kV to 157 kV) of a Canadian utility were periodically monitored. As part of the maintenance of the apparatus, the analysis of the furanic compounds is carried out. These are obtained by high performance liquid chromatography in a laboratory, with a regularity that varies according to the age of the transformer. This article deals with a transformer aging evaluation tool built from fuzzy logic. A fuzzy inference system is implemented by taking as input the values of 2-FAL and CO 2 /CO ratio. The tool helps directly providing the paper condition.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".