High Frequency Position Offset Injection Based PMSM Winding and Magnet Temperature Decoupled Estimation with Wide Speed Range
Bibliographic record
Abstract
In permanent magnet synchronous machine (PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperature dependent terms from the voltages using the machine model. The estimation accuracy under low speed or load can be greatly affected by the model uncertainty and noise due to low signal-to-noise ratio. This paper presents a high frequency (HF) position offset injection-based winding and permanent magnet (PM) temperature decoupled estimation approach for PMSMs to achieve accurate and robust temperature estimation among a wide speed range especially under low-speed conditions. In the proposed approach, a small HF position offset is injected into the machine to construct a decoupled winding and PM temperature estimation model, in which the winding and PM temperatures are independently estimated from HF excitations. The temperature estimation is independent from the fundamental model and parameter variation, and it achieves high signal-to-noise ratio under low-speed conditions. Moreover, the temperature estimation is also not affected by magnetic saturation and inverter distortion, which can improve the accuracy and robustness of temperature estimation. The proposed approach is validated with experiments and comparisons on a laboratory machine 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".