Understanding the Surface Discharge Characteristics with Thermally Aged Ester Fluid Impregnated Pressboard Adopting Fluorescent Fiber Technique
Bibliographic record
Abstract
Surface discharge activity in transformers has an effect to permanently degrade the insulation structure. Early detection of these discharge is crucial for system reliability where Ultra high frequency (UHF) sensor and fluorescent fiber technique show good signal to noise ratio and better sensitivity. An accelerated thermal ageing is carried as per IEC 61125 at a temperature of 160°C. Surface discharge inception voltage (SDIV) and Phase-resolved partial discharge (PRPD) studies have been carried out for thermally aged natural ester oil impregnated pressboard (OIP), by adopting fluorescent fiber technique in comparison with the UHF technique. The optical emission spectroscopy (OES) identifies the emission spectrum of elements during the discharge activity, where a machine learning tool is employed to classify the specimens based on its ageing period. Further, characteristic variation in properties of thermally aged pressboard were analyzed through thermal analysis and solid-NMR studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".