Correlated Inductance Modeling and Estimation of Permanent Magnet Synchronous Machines Considering Magnetic Saturation
Bibliographic record
Abstract
For permanent magnet synchronous machines (PMSMs), magnetic saturation is a key challenge to parameter estimation and saturation modeling using polynomials is popular with dq- axis inductances estimated independently. However, dq-axis inductances are mathematical terms derived from stator frame and are indeed correlated, which is not fully explored for improving estimation performance. Hence, this paper proposes a correlated inductance model to represent the relationship among dq- axis inductances and model their nonlinear variations with respect to stator currents due to magnetic saturation. The idea is to model the saturation in the stator frame and derive the correlated inductance model in the rotating frame. The estimation model is established based on the correlated inductance model, in which dq- axis inductances are represented using the same components and the correlation can be fully explored to improve the estimation accuracy. Moreover, the proposed estimation approach does not involve the division by the dq- axis currents, which can ensure the estimation accuracy especially under conditions with small current magnitude. While existing methods often involve the division and result in limited accuracy under such conditions. The proposed approach is evaluated on two test machines and compared with existing methods to demonstrate the performance improvement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".