A versatile power-hardware-in-the-loop based emulator for rapid testing of electric drives
Bibliographic record
Abstract
In this paper a power-hardware-in-the-loop (PHIL) based machine emulator is developed. The main utility of this PHIL based machine emulation system is in testing the driving inverter and controller of an electric drive system. This paper presents an inductive filter to interface the PHIL emulator and the driving inverter, simplifying the control of the proposed machine emulator, significantly. A detailed analysis of the machine emulator control to accurately emulate the machine model behavior is also presented. Furthermore, the machine emulator discussed in this paper, uses finite element analyses (FEA) based machine models, which allows emulation of the machine's geometric and magnetic characteristics, thus greatly improving the emulation accuracy. Real-time simulations are presented to validate the proposed machine emulator control and functioning, followed by experimental validation of the results with a surface-mounted permanent magnet synchronous motor (PMSM) coupled to a DC dynamometer. Experimental results are also presented in this paper to verify the machine emulator control with transients and bidirectional power flow capability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".