Closed-loop non-parametric model identification of synchronous generator using NARX polynomials
Bibliographic record
Abstract
System identification can be carried out by perturbing the system input(s) and processing the recorded input(s) and output(s) of the system. In synchronous generators, if the governor is not in action, one of the inputs (the mechanical torque) is not available for measurement, and the experiments cannot be carried out. In the published literature, the input perturbation is mostly carried out through the excitation system, and the mechanical torque is assumed to be constant during the experiment. This would affect the accuracy of the results. In this paper, various multivariable closed-loop identification methods (direct and indirect and linear and nonlinear) are used to obtain an accurate and comprehensive model for the synchronous generator. To simplify model structure, the orthogonal least squares with D-optimality method is used to remove unnecessary terms. A comparison of the performance of various techniques shows that closed-loop nonlinear identification using nonlinear auto regressive with exogenous input polynomials is very effective, and a simple nonlinear model for the synchronous generator can be identified successfully using multivariable closed-loop input and output data. Copyright © 2014 John Wiley & Sons, Ltd.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".