Average-Value Modeling of Line-Commutated AC–DC Converters With Unbalanced AC Network
Bibliographic record
Abstract
AC–DC line-commutated converters (LCCs) are widely utilized in existing and emerging ac–dc power systems. Analysis of such systems under balanced and unbalanced conditions is often carried out using electromagnetic transient (EMT) simulation programs. Therein, the detailed switching models of LCCs are often the computational bottlenecks for system-level studies. Recently, the parametric average-value models (PAVMs) have been developed to achieve fast and efficient simulations of switching converters. In this paper, the PAVM methodology is extended to consider operation of LCCs under unbalanced conditions in the ac network. This is done in the extended PAVM by formulating the ac-side harmonics in positive and negative sequences as well as the dc-side harmonics (i.e., ripples) with respect to the ac network imbalance. The new PAVM is validated experimentally and by simulations, and is demonstrated to be accurate in reconstructing the ac and dc waveforms under unbalanced conditions in the ac network. Meanwhile, the proposed PAVM is computationally much faster than its detailed switching model counterpart.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".