Aggregated and Reduced-Order Admittance-Based Modeling for Efficient Small-Signal Analysis of Power-Electronic-Based Power Systems
Bibliographic record
Abstract
Aggregation and model-order reduction techniques may be applied to parts of large-scale power grids where the detailed dynamics of individual components are not necessary, thus enhancing the efficiency of the overall simulation. This paper proposes an aggregated and reduced-order admittance-based modeling (ARO-ABM) that enables efficient and accurate time-domain simulations of power grids with converter-interfaced distributed energy resources (DERs). The ABMs of converter-interfaced resources (CIRs) with diverse structures and parameters are formulated as transfer functions. Then, the transfer-function-based ABMs of CIRs are aggregated along with their collector lines and impedance/ admittance-based model (I/ABM) of any other connected components, such as loads. The use of I/ABMs enables scalable aggregation of all CIRs, including those with fully known dynamic models, as well as those whose models may not be disclosed by manufacturers. This step is followed by the model-order reduction in the frequency domain. These steps result in the reduction of the computational complexity of the individual subsystems. The proposed method is demonstrated to enable the use of larger simulation time steps while maintaining good accuracy in offline (MATLAB/Simulink) and real-time (OPAL-RT) simulations of power-electronic-based power systems.
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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) | 0.001 | 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 | 0.001 |
| 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".