New Algorithm for Rotor Ground Wall Condition Assessment of Large Salient-Pole Generators
Bibliographic record
Abstract
Deterioration of ground-wall insulation is one of large salient poles generators failure modes. A failure in this insulation can seriously compromise the machine's operation, causing stray current circulation between poles, leading to vibrations, and resulting to costly unplan maintenance. Therefore, it is crucial to act before a failure occurs by planning an appropriate corrective maintenance. Monitoring the insulation to ground resistance by measurements is one important way to do so. Unfortunately, there are some challenges interpreting the results, such as the lake of references, insulation materials with different dielectric properties, the influence of the environment, making the assessment of the rotor insulation health a complex puzzle. Based on analysis of thousands of insulation resistance tests results from a large fleet of salient pole generators ranging from few to hundreds of MVA, a new algorithm is proposed to help assist decision makers. It will be shown that the interpretation of results could be standardized with algorithms leading to a comprehensive health index with a certain degree of confidence. This allows non-experts to have a clearer view of the rotor ground wall insulation condition and quick comparison of assets together. Moreover, it is proposed that these assessments can be conducted with the generator in service. Combining on-line and off-line tests could be a precious tool to quickly alert if a degradation occurs and reduce unit downtime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".