Efficient Permanent Magnet Temperature Modeling and Estimation for Dual Three-Phase PMSM Considering Inverter Nonlinearity
Bibliographic record
Abstract
Accurate temperature information is crucial to dual three-phase permanent magnet synchronous machine (DT-PMSM) drives. Therefore, this article proposes two efficient models for permanent magnet temperature estimation of DT-PMSMs. The proposed models are derived through current injection in the reference frame that does not contribute to torque production. Through current injection, the proposed models can fully explore the two sets of machine equations to cancel winding resistance and machine inductances. To improve the estimation performance, inverter nonlinearity is compensated in the first model and cancelled in the second model. In comparison to existing methods, the proposed approach is computationally efficient and robust to parameter variation, magnetic saturation, and inverter nonlinearity. Moreover, the current injection will not affect the machine torque production and control performance. The proposed estimation approach is evaluated on a laboratory DT-PMSM under various operating conditions.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".