A procedure to identify an accurate linear model of a synchronous machine
Bibliographic record
Abstract
Results of a nonlinear simulation is used in this paper to identify a linear model of a black-boxed synchronous machine. Linear system identification methods such as Prony Analysis, are employed to identify linear models of power system devices when their complete mathematical models are not available to be included in small signal stability studies. While the user lacks the knowledge of the control structure and its parameters, availability of the machine model is practically possible as the models and their parameter estimation methods are well established. Rapidly decaying transients of the machine, such as amortisseur winding transients, usually tend to be missed by available system identification methods. In this work an improved expanding-data-window Prony Analysis method is complemented with an eigenstructure assignment based method to incorporate a-priori knowledge of the generator to determine the highly damped transients of an electromagnetic transient (EMT) simulation model of a synchronous machine with generator controllers.
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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 | 0.000 |
| 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".