Dynamic Vector Modeling of Three-Phase Mutually Coupled Switched Reluctance Machines with Single dq-Quadrant Look-up Tables
Bibliographic record
Abstract
This paper presents a dynamic modeling method for a 3-phase mutually coupled switched reluctance machine (MCSRM) considering spatial harmonics, and saturation. The conventional modeling methods of MCSRMs are based on 3D look-up tables (LUTs), where the phase flux linkages are considered as state variables. These 3D LUTs describe the phase currents with respect to phase flux linkages, and rotor position. The 3D LUTs represent two $dq$ quadrants, and are obtained from finite element analysis (FEA) by multi-phase excitation where the excitation currents cover two quadrants in the $dq$ synchronous reference frame. The LUTs used in the proposed method represent the phase current, and electro-magnetic torque as vectors. The magnitude, and the angle of these vectors are represented by the sine, and cosine Fourier coefficients. Hence, rotor position is not an input to the LUTs, and the proposed method uses 2D LUTs. Additionally, the flux linkages in the four $dq$ quadrants possess symmetry for MCSRMs. Therefore, LUTs corresponding to only one $dq$ quadrant are required. The single-quadrant based LUTs reduces the number of FEA steps, and the size of the LUTs by 50% compared to the two-quadrant LUT based models. Finally, the proposed method is validated using FEA, and experiments for a 12/8 MCSRM.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".