Dynamic average modeling of line-commutated converters for power systems applications
Bibliographic record
Abstract
Detailed switch-level models of high-pulse-count converters are relatively straightforward to implement using commonly available simulation packages used for digital time-domain simulations and studying of power systems transients. However, such models are computationally intensive due to switching, and could become the bottle-neck for system-level studies with large number of components and controllers. This paper describes approaches for developing dynamic average-value models, i.e., the analytical derivations and parametric modeling. The resulting approximate models do not represent switching but still capture the transient behavior of the original converter circuit. The paper presents the results for 3- and 6-phase rectifiers implemented in PSCAD/EMTDC and Matlab/Simulink and shows that dynamic average models can be very effective. The paper also shows that as the number of pulses/phases increases, so does the complexity of switching pattern that defines the operating modes.
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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".