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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".