Assessing insulating oil degradation by means of turbidity and UV/VIS spectrophotometry measurements
Bibliographic record
Abstract
Oil is a vital part of the transformer body and (similarly to blood in a human being body) keeps responsibility for the condition of the entire organism. Oil is particularly responsible for functional serviceability of the entire insulation system. The insulating oil must be kept in pristine condition, since its condition can be a decisive factor, which determines the life span of the transformer. Fields and laboratory experiences have shown that transformer oil contains a vast amount of information. Oil analyses can be extremely useful in monitoring the condition of power transformers. To meet pressing needs of power industries, fast, inexpensive and reliable laboratory testing procedures are necessary. To ensure long-term reliability of oil filled power transformers, it is important to identify early sign of degradation of the insulating oil. In this paper, oil degradation was monitored with various ASTM test methods. Investigations were performed on service-aged oil samples as well as on oil samples aged in laboratory conditions. Many key parameters actually used to monitor the condition of transformer oil relative to oxidation/degradation were investigated. From the obtained results, correlations were found between some of them. The results indicate that Dissolved Decay Products (DDP) and turbidity, which change with a higher rate than interfacial tension (IFT) and Acid Number (AN) values, can be possibly used as an effective index for insulating oil degradation assessment. Limits are suggested which provide a “picture” of the fluid 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".