Aggregated and Reduced-Order Admittance-Based Modeling of Converter-Interfaced Resources for Power Systems Transient Analysis
Bibliographic record
Abstract
The proliferation of converter-interfaced resources (CIRs) in power grids has highlighted the need for their accurate and efficient modeling. This paper proposes an aggregated and reduced-order admittance-based modeling (ARO-ABM) method for multi-converter systems, aimed at achieving efficient time-domain simulations. First, the nonlinear subsystems of the grid-following CIR with slow dynamics, i.e., the phase-locked loop (PLL) and power controller, are linearized. Then, both the fast (linear) and slow (linearized) subsystems of the CIR system are integrated into a unified admittance-based transfer matrix model. Subsequently, the obtained admittance models of the CIRs, combined with the impedance of the collector lines, facilitate the proposed aggregated modeling of multi-converter systems. This aggregation, followed by model-order reduction, reduces the computations and simplifies the evaluation of the overall dynamic behavior of the multi-converter system, as seen from the external power network. The accuracy and computational improvement of the proposed ARO-ABM are verified through simulations using MATLAB/Simulink.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".