Numerically Efficient Average-Value Model for Voltage-Source Converters in Nodal-Based Programs
Bibliographic record
Abstract
Discrete detailed models of high-frequency switching voltage source converters (VSCs) are accurate but computationally expensive in simulations of large power-electronics-based systems. For fast/efficient studies, the average-value models (AVMs) of VSCs have proven indispensable, which conventionally utilize controlled voltage/current sources to interface with external circuits. In nodal-analysis-based electromagnetic transient (EMT) simulation programs with a non-iterative solution, the interfacing variables are computed based on the values of input voltages/currents calculated at the previous time step. This delay may cause numerical inaccuracy and/or instability at large simulation time steps. Recently, a so-called directly-interfaced AVM (DI-AVM) has been developed for VSCs that avoids this delay. In this paper, the formulation of the DI-AVM is generalized for an arbitrary configuration of the interfacing nodes. This is done by formulating the extended equivalent conductance matrix for the VSC AVM, assuming all nodes are floating. The generalized conductance matrix is then merged into the overall network nodal equation. The extended DI-AVM is verified in PSCAD/EMTDC against the traditional dependent-source-based AVMs under both balanced and unbalanced conditions and is demonstrated to outperform the conventional AVMs in terms of numerical accuracy at large time steps.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".