Slot–pole Selection for Concentrated Wound Consequent Pole PMSM with Reduced EMF and Inductance Harmonics
Bibliographic record
Abstract
Replacing half of the number of poles in a conventional surface permanent magnet synchronous machine (PMSM) reduces the use of rare earth materials delivering similar performance. However, in such a consequent pole configuration certain slot-pole numbers deliver high magnitudes of even order harmonics in the induced EMF waveforms leading to unbalanced magnetic force. Further, utilizing concentrated windings (CW) in the stator adds to this space harmonic content. In literature, slot-pole combinations were selected based on fundamental winding factor, cogging torque and net force on the machine, neglecting space harmonics content in inductance and induced EMF waveforms. Motivated by the drawbacks in the existing methods, in this paper, a novel inductance harmonics factor and EMF harmonics factor have been modelled using winding function method for a consequent pole multiphase CW PMSM. Furthermore, a gradient descent algorithm-based approach is implemented to optimally select slot-pole combination, with reduced inductance and EMF harmonics, for three-, five- and six-phase FSCW PMSM, with little prior knowledge about structural information of the machine. The inductance and induced EMF harmonics for optimal slot-pole combinations obtained from the algorithm are verified using finite element and experimental analysis.
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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".