Improving Performances of Interior Permanent Magnet Synchronous Motors by Using Different Rotor Angles
Bibliographic record
Abstract
The torque ripple, vibration and noise generated in interior permanent magnet synchronous machines (IPMSMs) due to the cogging torque.This will directly influence on the performance of the motors in general and IMMSMs in particular.Hence, the computation and investigation of the cogging torque play an important role for manufactures and designers to design the electric motors.In this paper, the analytical model of skew slot stators (SSSs), one-direction step-skew rotor (1D-SSR) and two-direction step skew rotor (2D-SSR) is presented to reduce the cogging torque of the IPMSMs.Then, the FEM is proposed to analyze and simulate the magnetic flux density, back electromotive force (EMF) and electromagnetic torque.However, in order to minimize the total harmonic distortions of the back-EMF waveforms due to the use of skew step rotors, a combination of the two-direction segment rotor structure with an optimal skewing angle of the 2D-SSR will be presented.A practical motor is applied to validate the developed method.The obtained results from the simulatated method are finally compared with the measured method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".