Direct Interfacing of Parametric Average-Value Models of AC–DC Converters for Nodal Analysis-Based Solution
Bibliographic record
Abstract
AC–DC converters are widely used in many power-electronic-based systems. There is an increasing need to simulate such systems using larger time-steps in offline and/or real-time electromagnetic transient (EMT or EMTP) simulators. The so-called parametric average-value models (PAVMs) have been developed to allow larger time-steps and provide fast simulations. However, the application of PAVMs in nodal-analysis-based EMTP programs typically requires a one-time-step delay between the interfacing sources and the network solution (i.e., indirect interfacing), causing inaccuracy and numerical instability at medium-to-large time-steps. This paper presents a direct interfacing method for PAVMs of line-commutated rectifiers (LCRs). The proposed method linearizes the PAVM interfacing equations and incorporates the respective sub-matrices and history terms into the network nodal equations, which eliminates the need for a time-step delay. Simulation studies verify the effectiveness of the proposed method in EMTP-type solution wherein very good accuracy and numerical stability is achieved at fairly large time-steps, which has not been previously possible with conventional methods.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".