Emulation of a Permanent-Magnet Synchronous Generator in Real-Time Using Power Hardware-in-the-Loop
Bibliographic record
Abstract
Permanent-magnet synchronous generators (PMSGs) are used in several applications of traction electrification such as power supplies for auxiliary systems and vehicle on-board range-extenders. In this paper, a PMSG is emulated using power hardware in-the-loop. This emulation scheme provides a method to eliminate risks and costs associated with the testing, prototyping, and validation of controllers for power converters and other parts of the traction system. The proposed scheme can be used for testing of motor drives' different parameters, thereby enabling the testing of a variety of electrical machine drives where the machine prototype is unavailable. The voltage output of the proposed emulator system replicates the voltage generated within the real-time model. The terminal voltage from the real-time model depends on the load connected to the emulator terminals; the current drawn from the emulator output terminals is sensed and fed back to enable the emulator to replicate the PMSG characteristics in steady state and transients. The performance of the system for nonlinear loads, such as a rectifier with a capacitor filter and transient conditions, is also experimentally verified and presented along with validation against a physical PMSG coupled to a dynamometer.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".