Induction Machine Parameterization From Limited Transient Data Using Convex Optimization
Bibliographic record
Abstract
This article identifies the parameters of an induction machine using limited and nonintrusive observations of available input voltages, stator currents, and the rotor speed. Parameter extraction is formulated as a nonconvex estimation problem, which is then relaxed to a convex conic optimization problem. While the resulting relaxation could exhibit a satisfactory performance, there might be cases where the solution of convex relaxation fails to satisfy the dynamic equations of the machine. This is remedied through a local search approach initiated using the solution obtained from the relaxed problem. The proposed method is experimentally verified on a squirrel-cage induction machine with missing measured data. Using the measured signals as the benchmark, time-domain transients produced by the parameters estimated using the proposed method show almost 20% better match compared to time-domain transients produced by the parameters obtained via conventional testing.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".