Linear Amplifier-Based Power-Hardware-in-the-Loop Emulation of a Variable Flux Machine
Bibliographic record
Abstract
Emulation of an electric machine allows the testing of a drive inverter and controller of an electric drive system, prior to the manufacture of a machine prototype. This paper presents the emulation of a variable flux machine (VFM). 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 emulator to mimic all machine magnetic and geometric behaviors such as saturation and torque ripple. In addition, 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 detailed control description for the proposed machine emulator system and a machine model look-up table data verification against a prototype 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 at different magnetization levels. Experimental results obtained from the emulator are subsequently compared against the experimental results obtained from a prototype VFM drive to verify 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) | 0.005 | 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".