Parameter Determination of PMSM Using Coupled Electromagnetic and Thermal Model Incorporating Current Harmonics
Bibliographic record
Abstract
With advent in permanent magnet synchronous machine (PMSM) structure and inverter topologies, accurate parameter determination is of significance for high-performance control, analysis, and making critical decisions on inter-dependent design parameter variations for machine optimization. However, the machine parameters, including permanent magnet (PM) flux linkage and dq-axis inductances, vary during operation with machine nonlinearities such as magnetic saturation, temperature rise, and the introduction of spatial- and time-harmonic contents contributing toward inaccuracies during machine parameter determination. While classical dq-axis modeling fails to accommodate non-sinusoidal winding distributions and the effects of temperature rise, finite-element analysis (FEA) is computationally expensive and coupling of electromagnetic and thermal analysis including current harmonics becomes complex. Therefore, in this paper, a novel magnetic equivalent circuit model incorporating the effects of temperature rise and current harmonics has been developed for parameter determination of PMSMs. A lumped thermal model is implemented to determine the temperatures at each point of the machine. The proposed coupled electromagnetic and thermal model has been validated for various operating conditions of a fractional-slot distributed wound laboratory PMSM with FEA and experimental investigations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".