A Two-Layer Network Equivalent With Local Passivity Compensation With Applications to Hybrid Simulations of MMC-Based AC–DC Grids
Bibliographic record
Abstract
A frequency-dependent network equivalent (FDNE) is essential to capture wide-band frequency dynamics in the hybrid simulation of large-scale modular multi-level converter based ac-dc grids. The FDNE model must be enforced to be passive, ensuring the numerical stability in time-domain simulations. However, existing passive enforcement techniques based on global optimization perturbations cannot guarantee convergence, accuracy, and efficiency simultaneously. To address the issues, a two-layer FDNE (T-FDNE) model is developed for the ac grids. The two layers, namely, detailed layer and equivalent layer, have their admittances derived by perturbation test and analytical approach, respectively. The passivity of the T-FDNE model is guaranteed by the proposed local passivity compensation technique using auxiliary rational functions. Since no optimization is required and the passivity is enhanced locally, the convergence, accuracy, and efficiency can be improved considerably. By incorporating the T-FDNE model into the interface model of transient (TS) and electromagnetic transient hybrid simulations, wide-band frequency interactions, especially those of very high frequency, can be reflected effectively. The performance (efficiency and accuracy) of the T-FDNE model as well as of the hybrid simulation method has been validated on a modified and practical ac-dc system in China.
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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 | 0.001 |
| 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".