Harmonic Current Optimization for Torque Ripple Reduction in Permanent Magnet Synchronous Machine Drives Based on Torque Ripple Surrogate Model
Bibliographic record
Abstract
Torque ripple reduction based on harmonic current injection has been developed for PMSM drives. In such methods, torque ripple model (TRM) or speed harmonics are used for harmonic current optimization, which have several limitations. The performance of TRM based methods is limited due to the accuracy of the model itself and machine parameters, which leads to the remaining torque ripples. The speed harmonic-based methods have speed limitations, since the speed harmonics generated by the torque ripples cannot be detected at high-speed operations. In this article, the torque ripples of PMSM drives are first described based on the surrogate model, which does not require machine parameters. Based on that, the numerical solution of optimal harmonic currents is obtained offline using particle swarm optimization, considering both torque ripple reduction and loss minimization. Since the torque ripples are predicted with the machine-parameter-independent model instead of the speed harmonic measurements, the impact of the inaccuracy in machine parameters and the analytical torque ripple model are removed, and the proposed method may be effective over a broader range of speeds. The proposed method is evaluated experimentally and demonstrated to have several advantages over existing alternative methods in reducing torque ripples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".