Novel Flux Linkage Estimation Algorithm for a Variable Flux PMSM
Bibliographic record
Abstract
This paper presents a novel algorithm for online rotor flux linkage estimation for a variable flux interior permanent magnet synchronous machine drive system at different flux density levels. A modified adaptive nonlinear filter is used to instantaneously estimate the amplitude, phase angle, and frequency of the major back EMF harmonic components, from which the total air-gap flux linkage is estimated. The algorithm avoids the averaging method that depends only on the fundamental back EMF component in estimating the air-gap flux linkage. The d-axis inductance versus current measurement test is performed at variable magnetization states to account for the d -axis inductance variation when estimating the rotor flux linkage. Since the stator winding resistance is temperature dependent, a thermocouple is used to obtain the actual stator winding temperature for accurate stator winding resistance measurement. The core loss resistance has been neglected for simplicity. The method was experimentally evaluated for different magnetization states and showed a good performance in tracking the rotor flux linkage variations. The method was tested for field-oriented control (FOC) schemes (id= 0) and for FOC with the negative d-axis current operation as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".