Non-real-time hardware-in-loop electromagnetic transient simulation of microcontroller-based power electronic control systems
Bibliographic record
Abstract
This paper introduces a new method of modeling microcontroller-based systems in a non-real-time electromagnetic transient (EMT) simulation. Presently, power electronic control system simulation relies chiefly on either simplified control block models in a non-real-time simulator or real-time hardware-in-loop simulation. Migrating control block models from the simulated design to actual microcontroller hardware requires significant additional effort. Real-time hardware-in-loop simulation requires complex interfacing using costly real-time hardware. The method described in this paper replaces control system blocks in non-real-time simulation with actual microcontroller code. The method enables both software-in-loop simulation and processor-in-loop co-simulation, eliminates the drawbacks of the simulation methods described above, and allows direct optimization of the actual controller hardware. The method is demonstrated by simulations of a three-phase ac motor controller in both software-and processor-in-loop configurations. The simulations are verified by laboratory results obtained using the tuned microcontroller hardware to implement a control system for a 3-phase ac motor.
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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 | 0.001 | 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".