Scanning Individual Stator Bars and Coils with an Antenna to Detect Localized Partial Discharges
Bibliographic record
Abstract
Partial discharge (PD) measurements have been carried out for years on generators, individual bars and form-wound coils. Even if the determination of quantitative criteria for what is considered normal and abnormal PD still represents a challenge in the industry, phase resolved partial discharge (PRPD) pattern recognition has been successfully used to recognize the nature of PD activity [1-3]. In the present work, conventional PD measurements using a capacitive coupler were compared with measurements made using an antenna to detect localized PD sites on individual bars and coils. The antenna was used to map localized PD sites along the entire length of the straight portion. The measurements were carried out on bars and coils of four different designs. The results revealed that the two measurement techniques give complementary information. It was found that the antenna had a limited sensitivity to PD occurring in micro-voids occluded within the groundwall insulation. For most of the bars and coils tested, the antenna did not detect any signal, even if there was always PD activity measured with the standard capacitive coupler. However, some of the specimens tested with the antenna revealed localized PD sites with distinctive PRPD patterns, suggesting that some anomalies were present at these sites. This information can thereafter be used to determine where to perform dissection of bars and coils.
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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) | 0.001 | 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 | 0.001 | 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".