Average-Value Modeling of Hybrid LCC-VSC HVDC Systems with Direct Interfacing in PSCAD/EMTDC
Bibliographic record
Abstract
Line-commutated converters (LCCs) and voltage-source converters (VSCs) are widely deployed in HVDC systems. Average-value models (AVMs) of such converters have proven to be numerically efficient for real-time/offline simulation studies where detailed switching models are computationally expensive. However, with a non-iterative solution, the conventional so-called indirectly-interfaced AVMs (IDI-AVMs) may lead to inaccurate or unstable results at large simulation time steps due to the one-time-step delay between the AVM input values and the interfacing variables. In this paper, the AVMs of LCCs and VSCs are directly interfaced with the external system to eliminate the one-time-step interfacing delay. The new directly-interfaced AVMs (DI-AVMs) of LCCs and VSCs are formulated in nodal form, and their resultant matrices are merged into the external system nodal equations. The effectiveness of the presented method is investigated on a hybrid LCC-VSC HVDC system in PSCAD/EMTDC. It is verified that the proposed DI-AVMs are numerically superior and more accurate than the conventional IDI-AVMs and allow simulations with much larger time-step sizes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".