Practical Testing Solutions to Optimal Stator Harmonic Current Design for PMSM Torque Ripple Minimization Using Speed Harmonics
Bibliographic record
Abstract
Torque ripple has been a critical issue in permanent magnet synchronous machines (PMSMs). After PMSMs are designed, an efficient way for torque ripple minimization is to inject an optimized harmonic current into the machine to produce an additional torque ripple to cancel existing torque ripple resulted from the imperfection of machine design and manufacturing. This paper proposes a testing-based approach for optimal harmonic current design by using the speed harmonics. In particular, two testing solutions (TS), TS-A and TS-B, are proposed. In TS-A, two test signals are injected into the machine, and the optimal harmonic current is calculated from the test signals as well as the measured speed harmonics. In TS-B, two test signals are injected and adjusted to achieve the designed objective in terms of speed harmonics, and the optimal harmonic current is calculated from only the test signals. These two solutions can find the optimal harmonic current to minimize the torque ripple with minimal machine losses. Moreover, machine parameters are not required in the optimal harmonic current design, so the proposed solutions are not affected by the machine and drive nonlinearities. Compared with existing approaches, the proposed solutions have advantages in terms of computation efficiency and simple implementation. The proposed solutions are evaluated on a laboratory PMSM drive system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".