Non–Dominated Sorting Genetic Algorithm Based Investigation of Optimal Odd Slot Numbers for Stator Shifted Fractional–Slot Wound PMSMs
Bibliographic record
Abstract
Stator slot shifting is widely implemented in fractional-slot wound permanent magnet synchronous machines for reduction of high spatial harmonic contents resulting in improved efficiency and flux-weakening performance. Existing method involves using twice the number of stator slots present thereby resulting in a winding topology with a coil pitch of 2. However, in this process, many slot-pole combinations, especially with odd slot numbers are neglected. Therefore, this paper proposes a procedure for stator slot shifting for n-phase fractional-slot wound machines considering all feasible slot-pole combinations. The slot-plot combinations are analyzed for performance attributes including winding factor, total harmonic distortion, minimum cogging torque and maximum torque per unit winding losses. Further, a non-dominated sorting genetic algorithm is used for optimal slot-pole selection for each phase number. The resultant machine performance characteristics of a sample best design candidate is verified using experimental data and finite element 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".