Adagrad Algorithm based Optimal Slot-pole Selection for Reduced Inductance Harmonics in Concentrated Wound Multiphase PMSM
Bibliographic record
Abstract
Fractional slot concentrated windings (FSCW) permanent magnet synchronous machines (PMSM) have high content of space harmonics in the magnetomotive force (MMF) due to which the harmonic inductance is much larger than the magnetizing inductance [1]. These inductance harmonics lead to high torque ripple and low power factor. In case of FSCW, the coils are full pitched and cannot be chorded like in distributed windings to reduce inductance harmonics and also a suitable rotor structure have small impact on reduction of these harmonics. However, the space harmonic content in the FSCW PMSM vary significantly with the choice of slot-pole combination. Thus, the inductance harmonics can be modeled and minimized using an optimal choice of machine phases (m), stator slot numbers (S) and rotor poles (P). State of the art: [2] has presented the selection of slot, pole and phase numbers for reducing harmonic leakage inductance specifically for single layer CW PMSM. In [3], a detailed procedure for slot-pole selection based on inductances for single and double layer windings are provided. However, these are restricted for odd phase numbers and the selection process is time consuming. In this paper, the impact of winding layers, phase belt, slots, poles, and phase numbers on inductance harmonics has been studied. Further, an Adapative gradient (Adagrad) algorithm based approach is implemented to optimally select these parameters with little prior knowledge about the structural data.
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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) | 0.001 | 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 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".