Using DFR Measurements for the Condition Assessment of Stator Winding Insulation Systems
Bibliographic record
Abstract
This publication presents the results of investigations that were performed to verify if the use of low-voltage dielectric frequency response analysis can be successfully applied to detect the deterioration of stator winding insulation systems. Three newly manufactured stator coils with a rated voltage of 13.8 kV were aged using the thermal cycle testing procedures described in IEEE 1310 and an extended and modified version of voltage endurance testing described in IEEE 1553, until dielectric failure. Throughout the accelerated ageing process, several dielectric measurements were performed, and the results trended over time. The results of the dielectric frequency response measurements were then compared with conventional dissipation factor measurements and partial discharge measurements to assess the sensitivity of dielectric frequency response analysis applied to stator insulation. This paper also discusses the influences of the nonlinear end potential grading material on the measured dielectric losses. Additional experiments are summarized to illustrate how this nonlinear material behaves depending on the applied voltage magnitude and frequency. Some examples of measurements performed on complete stator windings are also shown and discussed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".