Multi-Parameter Estimation of PMSM Using Differential Model With Core Loss Compensation
Bibliographic record
Abstract
Accurate parameters are critical to permanent magnet synchronous machine (PMSM) drive. This article investigates accurate flux linkages, inductances, and PM flux linkage estimation for PMSM with core loss compensation. With conventional model, core loss will induce flux linkage error especially in deep saturation region. Hence, this article first proposes a novel differential modeling technique to compensate core loss, in which differential measurement is defined as the incremental value calculated from the actual measurements under two different speed conditions. With multiple differential measurements, the flux linkage error due to core loss can be compensated to improve the accuracy of flux linkage estimation. Then, the polynomial-based flux linkage model is used to derive PM flux linkage and cross-saturation inductances. Self-inductances are estimated from the flux linkage model using least-squares method. The proposed approach can accurately estimate parameters without the need of core loss data and can improve the estimation accuracy especially in deep saturation region, which is validated on a laboratory interior PMSM and compared with the existing methods under various operating conditions.
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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 | 0.001 |
| 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".