Genetic Algorithm-Based Current Optimization for Torque Ripple Reduction of Interior PMSMs
Bibliographic record
Abstract
This paper investigates the torque ripple modeling and minimization for interior permanent magnet synchronous machines (PMSMs). At first, a novel torque ripple model is proposed in which the torque ripples resulted from the spatial harmonics of permanent magnet flux linkage, time harmonics of stator currents and the cogging torque are included. Based on the torque ripple model, a genetic algorithm (GA)-based harmonic current optimization approach is proposed for torque ripple minimization. In this approach, GA is applied to optimize both the magnitude and phase angle of the stator harmonic currents to minimize the peak-to-peak torque ripple, minimize the sum of squares of the harmonic currents, and maximize the average torque component produced by the injected harmonic currents. The results demonstrate that the magnitude of the harmonic current can be significantly reduced by optimizing the phase angles of these harmonic currents. This leads to further suppression of the torque ripple when compared with that of a case where phase angles are not considered in the optimization. Also, an increase of the average torque is achieved when the optimum harmonic currents are injected. The proposed model and approach are evaluated through both numerical and experimental investigations on a laboratory interior PMSM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".