Torque Harmonic Minimization Optimal Pulse Pattern Modulation Technique for Permanent-Magnet Synchronous Motors
Bibliographic record
Abstract
Permanent-magnet synchronous motors (PMSMs) inherently exhibit torque oscillations due to their magnetic properties, impacting performance and causing increased noise and vibrations. While pulsewidth modulation (PWM) techniques commonly minimize current harmonics, they often overlook torque fluctuations arising from the interaction between current and back EMF harmonics in PMSMs. Therefore, achieving both minimal torque harmonic content and reduced switching frequency is essential to obtain a smooth torque and low switching losses. This article introduces a novel low-frequency optimal pulse pattern-based torque harmonic minimization (OPP-THM) method, designed to directly minimize torque harmonics in PMSMs, rather than solely focusing on current harmonics. By considering back EMF harmonics in the optimization of switching angles, the proposed approach effectively suppresses low-order torque harmonics without compromising the contribution of back EMF harmonics to average torque, thereby enhancing the PMSM performance. The effectiveness of the proposed method is demonstrated through validation on a 4-kW experimental PMSM drive employing field-oriented control (FOC) under steady-state and transient 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".