An Analytical Solution to Optimal Stator Current Design for PMSM Torque Ripple Minimization With Minimal Machine Losses
Bibliographic record
Abstract
This paper investigates torque ripple minimization for permanent-magnet synchronous machines (PMSM), and proposes a novel analytical solution of optimal stator current design for torque ripple minimization. The proposed design is theoretically proven to be able to minimize the torque ripple with minimal machine losses. Moreover, the optimal stator current is computed from analytical expression, which is computationally efficient. Therefore, the proposed approach is applicable for torque ripple minimization under both transient state and steady state. However, existing approaches usually employ optimization algorithm to optimize the stator current, which is computationally complex and involves iterative computation, so their applicability is limited under transient state, because the optimal stator current must be adaptively updated with respect to operating conditions. Moreover, magnetic saturation is considered in the proposed approach by employing a novel linear model to model the relation between the inductance and the stator current under maximum torque per ampere (MTPA) control. In this way, the proposed analytical solution does not involve inductance, and thus, the influence of magnetic saturation can be effectively reduced. The proposed approach is validated on a laboratory PMSM drive system under both transient state and steady state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".