Admittance-Based Aggregated Modeling of Converter-Interfaced Resources With Heterogeneous Parameters for Transient Analysis of Multiconverter Systems
Bibliographic record
Abstract
Aggregated modeling of converter-interfaced resources (CIRs) can decrease the computational complexity in time-domain simulations of electric grids with high penetration of renewable sources. This article presents admittance-based aggregated modeling (ABAM) for grid-following CIRs. The aggregation is carried out by representing the CIRs’ current controllers and output filters using transfer functions and aggregating them as admittances and sources. For improved aggregation accuracy, the CIRs are grouped in terms of their ratings, synchronization system parameters, and collector system parameters. The numerical advancements of the proposed ABAM are shown in an example renewable energy system consisting of multiple grid-following CIRs. It is verified that the ABAM has low sensitivity to the parameters and excellent accuracy in capturing the dynamics of heterogeneous CIRs compared to the conventional preserved-structure aggregated model with weighted-mean parameters. The proposed ABAM also permits the use of large time-step sizes with acceptable numerical accuracy in (offline) MATLAB/Simulink and (real-time) OPAL-RT simulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".