Flux estimation of induction machines with the linear parameter-varying system identification method
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Bibliographic record
Abstract
In indirect field orientation control (FOC) methods, the magnitude and direction of the rotor flux are estimated from measurements of stator voltages, stator currents and the angular velocity of the shaft using a parameter model of the induction machine. However the performance of indirect FOC methods is dependent on the accuracy of the machine model and is therefore sensitive to variations in motor parameters such as the rotor resistance and the magnetizing inductance. Motor parameters vary greatly with temperature, frequency and current amplitude. This paper presents a novel method for estimating the rotor flux in an induction motor. Subspace identification methods are used to construct a linear parameter-varying (LPV), discrete time model of an induction motor based on measurements of the stator voltages and currents and of the angular velocity of the shaft. The identification algorithm has been tested on data obtained from a nonlinear, continuous-time simulation model.
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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 it