FTIR and FDS assessment of mineral oil under low electrical discharge
Bibliographic record
Abstract
Transformers are crucial elements in the transmission and distribution of electrical energy.The importance of diagnosing these equipments are two-fold: (1) the necessity of service reliability and (2) the likelihood to avoid economic and environmental concerns.Under service conditions, the electrical and thermal stresses or chemical contaminants may degrade the insulation oil inside the transformer and cause incipient failures or reduce its service life.Partial discharges well recognized to be among the most common stresses that can lead to slow but steady degradation of insulating oil in transformers.The present work aims at understanding the influence of low energy electrical discharge on mineral oil based on two spectroscopic methods: FTIR spectroscopy and Frequency Domain Spectroscopy (FDS).An electrical fault has been created by continuous discharge of 10 kV on the surface of various oil samples according to the ASTM D6180.From the FDS results, it was found that the amount of charge carriers and moisture increased with the aging time elapsed that influences the conduction phenomena and in turn, increases the dissipation factor.These results are confirmed by the FTIR results, which show that the intensity of the peak absorbance of the C-H and C-C functional group decreased with aging.The application of these two methods may help monitoring the condition of oil.A combined FTIR and FDS measurements highlighted the correlations between modifications in electrical properties and changes in the chemical structure of the oil under electrical accelerated ageing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".