An Accurate Core Loss Model of Inverter-Fed Induction Machine Considering Supply and Saturation Harmonics
Bibliographic record
Abstract
This article presents a novel mathematical model for accurately predicting the net core loss of inverter-fed induction machines (IMs). Rotating field waves generated by all the sources such as permeance variations, source harmonics, and magnetic saturation are derived using the material characteristics. Analytical expressions for additional surface core loss and pulsation losses generated by the saturation as well as the losses incurred by augmented teeth flux densities with leakage fluxes are derived. For accurate estimation of these losses, instantaneous filed densities in various iron segments at different loading conditions are determined with on-load magnetizing current in inverter-fed operation, calculated using time-domain variation of magnetizing inductance with flux linkage. Magnitudes of saturation caused field waves are then determined iteratively using the iron magnetization profile. The accuracy of the loss model is validated by comparing the measured and simulated core loss of 11 kW IM under no-load and on-load conditions. In the pursuit of achieving net-zero carbon emissions, advancing transportation electrification stands as a crucial milestone, necessitating the utilization of traction motors tailored. As such, a precise iron core loss model is proposed, capable of effectively accounting for frequency-dependent impacts in forecasting no-load and on-load core loss.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".