Analytical modelling and parametric sensitivity analysis for the PMSM steady‐state performance prediction
Bibliographic record
Abstract
Inaccuracy in the permanent magnet synchronous motor (PMSM) steady‐state performance calculation corresponds to the parameter error and model imprecision. Accurate determination of the PMSM parameters may encounter various complications because of its rotor structure and drive design. Therefore PMSM performance calculation is generally vulnerable to inaccuracy because of the parameter error. This article studies the effect of parameter error on the inaccuracy of the performance calculations. Several methods for determining the PMSM armature resistance, flux linkage constant and d ‐ and q ‐axis inductances with varying level of accuracy are proposed. The presented methods are applied to a laboratory PMSM and the sensitivity of the PMSM output power to the equivalent circuit parameters is analysed based on the experimental results. In addition, this study contributes to accurate performance estimations of the PMSM by developing a precise model that incorporates the saturation saliency and core losses. The accuracy of the proposed model is compared with the conventional dq ‐axis model and its higher accuracy is validated through experimental results.
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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 | 0.006 |
| 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) | 0.002 | 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".