A case study of condition-based maintenance of a 202-MVA hydro-generator
Bibliographic record
Abstract
Over the last few decades, partial discharge (PD) measurement has proven to be one of the most useful tools to identify degradation mechanisms of stator windings in air-cooled generators. Under normal operating conditions, PD levels and, more importantly, PD trending can indicate the presence of not only electrical but also mechanical, thermal or ambient (contamination) degradation processes. Hydro-Québec uses a 2D PDA (Partial Discharge Analysis) technique as its first line of diagnostics during yearly measurements on more than 120 large generators in its fleet. When a PDA measurement reveals a problem, such as high PD levels or sudden increase in level, the Phase Resolved Partial Discharge (PRPD) technique is used to improve the identification of the nature of the active PD sources. This paper presents results of on-line PD measurements from both the PDA and the PRPD techniques on the case study of a 13.8-kV 202-MVA generator. Identification of slot discharge activity in the PRPD patterns in this generator triggered a more detailed diagnostic. In-situ off-line measurement of the electrical contact between individual bars and stator core was performed on selected bars in order to confirm the presence and extent of a semi-conductive coating problem.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".