Power-Hardware-in-the-Loop Based Emulation of a Variable Flux Machine
Bibliographic record
Abstract
Electric machine emulation allows testing of the drive inverter and controller of an electric drive system, prior to manufacture of a physical machine prototype. This paper presents the emulation of a variable flux machine (VFM). In VFMs, air-gap flux can be controlled by changing the magnetization level of the magnets using a current pulse; leading to several advantages over permanent magnet (PM) machines. The VFM emulator system proposed in this paper uses a detailed look-up table based machine model for the purpose of emulation. This allows the machine emulator to mimic all machine magnetic and geometric behaviors such as saturation and torque ripple. Additionally, the machine emulator proposed in this paper uses high-performance high-bandwidth linear amplifiers as power amplifiers. This enables a high bandwidth (leading to a high accuracy) machine emulation. A control description for the proposed machine emulator system and a look-up table data verification of the VFM against a physical machine is initially presented. Experimental results are then presented to validate the utility of the proposed VFM emulator system to emulate various machine transient behaviors. Experimental results obtained from the emulator are subsequently compared against the experimental results obtained from a physical VFM drive to comment on the emulation accuracy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".