Multi-Resolution Modeling of Power Electronics Circuits Using Model-Order Reduction Techniques
Bibliographic record
Abstract
Highly detailed models of power-electronic converter circuits can be slow to simulate due to the wide disparity in transient time scales. This paper presents a framework for multi-resolution simulation of switching converter circuits by providing an appropriate amount of detail based on the time scale and phenomenon being considered. In this approach, a detailed full-order model that accounts for the higher-order effects of components, parasitics, switching nonlinearity (e.g., saturated inductors), switching event detection, etc., is constructed first. Efficient order-reduction techniques are then used to extract several lower order models for the desired resolution of the simulation. The simulation resolution can be adjusted as needed even during a simulation run time. The state continuity across different resolutions and switching events is ensured using appropriate similarity transforms. The proposed high-fidelity model of converter is verified with hardware measurement and is used to verify different simulation resolutions. The proposed methodology is demonstrated to achieve orders of magnitudes improvement in simulation speed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".