A Universal High-Frequency Induction Machine Model and Characterization Method for Arbitrary Stator Winding Connections
Bibliographic record
Abstract
High-frequency modeling of induction machines plays an important role in investigating motor drive electromagnetic interference issues such as stator winding reflected-wave overvoltage and bearing discharging current. Characterization of high-frequency machine models requires measurements of machine's differential-mode (DM) and common-mode (CM) impedances up to tens of MHz. The machine's stator winding connections, e.g., single-, and series-, parallel-winding Y/Δ configurations, influence the measured DM and CM impedances and model parameters. In this paper, a universal high-frequency equivalent circuit model capable of representing induction machines with arbitrary stator winding connections is proposed. The new model features a simple structure with a straightforward characterization method. Specifically, only one stator winding configuration is required for impedance measurements to fully characterize the machine model for arbitrary stator winding connections. The proposed methodology is demonstrated using a 7.5 hp dual-voltage nine-terminal/lead induction machine and a drive system. The simulated DM and CM impedances as well as the motor overvoltages show excellent agreement with the experimental results. The proposed model and characterization method represent significant improvement in terms of accuracy, applicability/generality, and convenience compared to prior conventional models.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".