Online Inductance Estimation of PM-Assisted Synchronous Reluctance Motor Using Artificial Neural Network
Bibliographic record
Abstract
A Permanent Magnet-Assisted Synchronous Reluctance Motor (PMASynRM) is favored because of its lower Permanent Magnet (PM) amount. They utilize more reluctance than PM torque compared to Interior Permanent Magnet Synchronous Motor (IPMSM) and Surface Permanent Magnet Synchronous Motor (SPMSM). Understanding the motor parameters at each operating point is crucial to achieving maximum efficiency. Variation of motor parameters due to temperature using offline models has been reported in the literature. These methods are computationally intensive, especially when the effect of cross-saturation is included in the models. Online parameter estimation is more impressive in real applications to develop a high-performance control technique as a result of these limitations, particularly when motors confront highly nonlinear structures such as PMASynRMs. An Artificial Neural Network (ANN) is presented in this paper for online estimation of inductances at the Rotor Reference Frame (RRF) to ensure optimum performance for model-based control systems. An online-tuned dynamic model is implemented by the proposed ANN and compared with Finite Element Analysis (FEA) data and experimental validation tests.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".