Characterization of a Variable Flux Machine for Transportation Using a Vector-Controlled Drive
Bibliographic record
Abstract
Variable flux machines (VFMs) have one more dimension for the control and performance improvement of the drive. The magnet flux in the VFM can be controlled to optimize motor performance. Software packages used in the design and performance evaluation of variable flux permanent magnet (PM) machine use a single demagnetization curve and recoil the operating point based on a linear recoil line parallel to the original demagnetization curve. Since the magnets like AlNiCo that is used in VFM have a nonlinear demagnetization curve, performance prediction of a VFM designed using finite-element software needs experimental verification. This paper presents a method based on vector control for the characterization of a VFM. An existing drive is used to measure the dq inductances of the machine at several magnetization levels, including the cross-magnetization effects. The same technique is extended to measure the torque-ripple and the torque-angle characteristics of the machine at different magnetization levels. Experimental results are provided for a 5-hp variable flux PM machine. The proposed method uses an existing drive to perform the tests, requiring no additional test setup.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".