Data-Driven Modeling and Compensation Strategy of PMSM Considering Core Loss and Saturation
Bibliographic record
Abstract
Accurate model of permanent magnet synchronous machine (PMSM) is significant for high-performance control. The machine model can be affected by various factors such as magnetic saturation and core loss effect, especially in the deep saturation and high-speed regions. This article proposes a data-driven-based machine modeling and compensation approach to improve the model accuracy by considering saturation and core loss effect. In the proposed approach, magnetic saturation is initially modeled using nonlinear polynomials and core loss effect is modeled with various speed data. The model mismatch due to these effects is then derived to generate the training data for the neural network (NN), which can accurately predict the model mismatch under various operating conditions. In comparison to the conventional model, the proposed approach adds compensation terms directly to the machine models, which can achieve better accuracy with efficiency and simple implementation, which can be utilized in motor control and parameter estimation. The proposed approach is validated on a laboratory interior PMSM and compared with existing methods 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) | 0.000 | 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) | 0.001 | 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".