Neural Network-Based Aggregated Modeling of Multiple Synchronous Generators with Heterogeneous Parameters
Bibliographic record
Abstract
Aggregated modeling of multiple synchronous generators (SGs) can enhance the efficiency of electromagnetic transient (EMT) simulations when the dynamic behavior of individual SGs does not require detailed investigation. This paper proposes a neural network (NN)-based aggregated modeling (NNB-AM ) method for the time-domain simulation of multiple SGs with heterogeneous parameters connected through diverse collector lines to the point of common coupling (PCC). The general NN modeling rule considers the PCC three-phase voltages as inputs, and the SGs’ equivalent three-phase injected currents and the equivalent rotor angle as outputs. The long short-term memory (LSTM) structure is adopted to implement and train the NN model using the dynamic responses generated from offline EMT simulations, which employ the non-aggregated classical full-order qd model of SGs. It is demonstrated that for a multi-SG-infinite-bus system, the proposed NNB-AM accurately captures the equivalent dynamic behavior of SGs while offering greater computational efficiency compared to the non-aggregated model counterpart.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".