The Correlation Between PRPD Patterns and Dissection on Individual Stator Coils
Bibliographic record
Abstract
In order to ensure the quality of stator windings, Hydro-Québec introduced specific diagnostic tests such as partial discharge (PD) measurements and dissections of individual bars and coils to the qualification program. Although PD measurements have been used for decades, this diagnostic test is still a matter of discussion, mostly because there is no standard procedure or rules specifying how to quantify the detected PD activity. However, there is an ongoing effort in the draft of the IEEE P2465 by the working group to propose a set of criteria to measure individual bars and coils. In the present work, quantification of PD activity on individual coils were made using phase resolved partial discharge (PRPD) patterns obtained using the combination of two types of PD sensors, a capacitive coupler and a near field probe. Parameters extracted from PRPD patterns were used to identify all active PD sources within the insulation system. Results indicate that PRPD patterns obtained in the lower frequency range with the capacitive coupler give the overall activity of all active PD sites within the insulation system. When PD sites were detected at higher frequencies, PRPD patterns made with the capacitive coupler correlate very well with those obtained with the near field probe in the same frequency range. Measurement with the near field probe in this range makes it possible to localize those PD sites within the insulation system. The combination of PRPD patterns obtained with both types of PD sensors is a powerful technique to identify the active PD sources within the insulation system of individual bars and coils. To validate the identification of the detected PD activity, dissections using microscopic observations were performed on two coils. In both cases, microscopic observations confirmed the PRPD recognition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".