Hybrid Parametric Average-Value/Detailed Modeling of Line-Commutated Rectifiers
Bibliographic record
Abstract
Simulations and analysis of power-electronic-based systems are conventionally done using detailed switching models of power-electronic converters that are available in many electromagnetic transient (EMT) simulation programs. Although being accurate, such detailed models typically require small time-steps for accurate detection and handling of switching events, which makes them computationally expensive. Recently, a parametric average-value modeling (PAVM) approach has been developed for system-level modeling and fast simulations of line-commutated rectifiers (LCRs) including several selected ac harmonics. In this paper, a new hybrid parametric methodology is presented, which has the capability of operating at large time-steps while including the details of the ac- and dc-side variables similar to the detailed switching models (but without the need for locating switching events, i.e., zero crossings), or operating as an average-value model. Extensive simulation studies demonstrate advantageous numerical efficiency and accuracy of the proposed hybrid parametric AVM/detailed model compared to the previous PAVMs as well as the detailed switching models of LCRs when using large time-steps for system-level studies.
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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) | 0.001 | 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".