Investigation of permanent magnet flux linkage variation in PMSMs due to temperature rise and magnetic saturation
Bibliographic record
Abstract
Accurate information of permanent magnet (PM) flux linkage is of significance to high-performance control and condition monitoring of permanent magnet synchronous machines (PMSMs). During machine operation, the PM flux linkage can vary due to temperature rise and saturation. Thus, this paper investigates how temperature rise and saturation influence the PM flux linkage under different operation conditions. Under no-load condition, the PM flux linkage is estimated from the back-EMF test. Under load condition, a speed harmonic based PM flux linkage estimation approach is proposed, in which the PM flux linkage is estimated from the speed harmonic without requiring machine parameters. Thus, the proposed estimation approach is not affected by the machine and drive nonlinearities and thus can guarantee the estimation performance. The proposed approach is applied for PM flux linkage estimation under various loads and temperatures to investigate the influence of temperature rise and saturation on the PM flux linkage. Experimental results demonstrate that the PM flux linkage under no-load is larger than that under load due to magnetic saturation, the PM flux linkage under load decreases as the saturation level increases, and the one under both load and no-load decreases linearly as the PM temperature increases.
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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 | 0.000 |
| 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 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".