Flux Linkage Tracking-Based Permanent Magnet Temperature Hybrid Modeling and Estimation for PMSMs With Data-Driven-Based Core Loss Compensation
Bibliographic record
Abstract
For permanent magnet synchronous machine (PMSM) drive, accurate magnet temperature is critical. The popular model-based magnet temperature estimation can be affected by core loss effect especially in the high-speed conditions. This article proposes a novel hybrid approach for accurate magnet temperature modeling and estimation, in which the estimation model is established by tracking the flux linkage variation, while the data-driven-based model is proposed to compensate the core loss effect. Specifically, the flux linkages in the rotating frame are projected into a new frame to derive the estimation model establishing the relationship between flux linkage variation and magnet temperature, in which the inverter distortion effect is canceled to improve the model accuracy. Based on this estimation model, the core loss effect is modeled, which indicates that the core loss influence is highly nonlinear and dependent on operating conditions. Hence, a radial basis function-based network is employed to model and compensate the core loss effect, and the network training is derived from the proposed model. The proposed hybrid approach can effectively improve the estimation performance especially at the high-speed conditions. Extensive experiments and comparisons are conducted on a laboratory interior PMSM drive to evaluate the proposed approach 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".