Effects of PWM excitations on iron loss in electrical steels and machines
Bibliographic record
Abstract
The iron loss in the ferromagnetic cores of electrical machines under PWM excitations could be many times higher than the loss under sinusoidal excitation. Therefore, the effects of the PWM waveforms, and their various characteristics (modulation index, switching frequency, and topology, etc.), on the iron loss must be understood and the ability to model these effects in the electrical machine design process is highly desired. In this work, the iron loss is reported for various electrical steels under PWM excitations and the effects of PWM waveform characteristics are studied. The iron loss measurements in a surface mounted permanent magnet motor under PWM excitations are also presented and are in agreement with the measured iron losses in electrical steels. The effect of the iron loss variations on the operating point of the machine is also discussed from the electric vehicle point of view. In the end, a computationally efficient approach based on the static Preisach model is presented to predict the iron loss in electrical steels. The proposed approach can model the effects of changing switching frequency, switching topology and modulation index on the iron loss with a reasonable accuracy.
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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".