Flexible medium voltage stator coil insulation system for on-site winding
Bibliographic record
Abstract
The dielectric performance of a fully cured non-VPI flexible stator coil insulation system was investigated by performing winding trials and series of dielectric tests. Different dielectric tests performed were voltage endurance (VE), surge test and thermal cycling. VE and thermal cycling tests are well understood and industry recognized test methods to qualify new or improved insulation systems. These are reliable test methods for comparing new insulation systems with service proven insulation systems. To develop a life curve, VE testing was performed at four different voltage levels and the acquired data was reduced using Weibull analysis. To demonstrate the impact of frequent starts and stops and to determine the effects of temperature rise, a thermal cycling test was performed following IEEE Std. 1310. Diagnostic, AC hipot withstand and VE testing after completion of 500 thermal cycles showed positive results. Stable partial discharge (PD), dissipation factor (DF), and tip-up results were recorded through the thermal cycling testing, which indicated no change in the insulation performance due to thermal cycling. In addition, VE testing after completion of thermal cycles showed promising results and met the requirements as outlined in the IEEE Std. 1553.
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 | 0.000 | 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) | 0.002 | 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".