Using structured laser illumination planar imaging (SLIPI) as a new technique to monitor the degradation of biodegradable oils in electrical power transformers
Bibliographic record
Abstract
Abstract The aging process of the insulating oils of an electrical transformer is initiated as soon as the transformer is put into service. The quality of these oils must therefore be rigorously evaluated to have reliable and exploitable data for decision‐making. In general, the decision is to continue monitoring, reclaiming/regenerating, or replacing the oil in extreme cases. Thus, early diagnosis of power transformer oils helps prevent potential breakdowns that could considerably impact the electrical energy transmission and distribution network. This research used an imaging technique called SLIPI (Structured Laser Illumination Planar Imaging) to accurately determine the extinction coefficient in different samples of optically dense biodegradable oils (natural and synthetic esters). The variation in the extinction coefficient as a function of the aging of these biodegradable oils under test has been investigated. The results indicate that the SLIPI is reliable as a diagnostic tool for biodegradable oils in power transformers. This technique could therefore be an alternative solution to the conventional monitoring methods.
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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 | 0.001 | 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) | 0.000 | 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".