Correction Factor for Insulation Resistance of Salient Poles
Bibliographic record
Abstract
Resistance of insulating material is known to be temperature dependent, the resistance decreasing as the temperature goes up. When applied to rotating machines, there is a need to establish a reference criterion of acceptance. For the resistance of an insulating material, the reference temperature is 313 K (40 °C). Depending on the insulation system, the correction factor will differ. The correction factors proposed in standard 43-2013 for thermoplastic and thermoset materials recommended for machine windings are given although it is a stator or a rotor winding. Even if the insulation system of a rotor salient pole can be composed today of epoxy (thermoset), it is slightly different from the insulation system of the stator winding, and to our knowledge it has never been proven that the same temperature correction applies to stators and rotors. The goal of this study is to present results of the insulation resistance of rotor salient poles as a function of the temperature and verify if a new correction factor is better suited for the rotor winding insulation system. To achieve this, experimental data of insulation resistance (IR) vs temperature will be shown for aramid paper and NEMA G11 insulation material. Experience with real rotor poles equipped with Fiber Bragg Grating (FBG) measurement systems and laboratory results on typical rotor insulation material will be presented. The results show that NEMA G11 material needs new correction factors based on the results.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".