Model-Based Spatial Harmonics Vector Compensation Method for Three-Phase Mutually Coupled Switched Reluctance Machine With Sinusoidal Current Excitation
Bibliographic record
Abstract
This paper presents a spatial harmonics compensation method for mutually coupled switched reluctance machines (MCSRMs) with sinusoidal current excitation. The regulation by linear controllers to achieve sinusoidal currents in MCSRMs is challenging due to the substantial presence of spatial harmonics. The standard vector control with using Proportional-Integral controllers cannot effectively suppress the spatial harmonics of the current waveform due to the bandwidth limitations. In the proposed method, the essential voltage harmonics are added to the fundamental voltage component to create sinusoidal currents. The voltage harmonics are calculated from the flux linkage harmonics where voltage and flux linkage harmonics are represented as vectors in terms of Fourier coefficients. The Fourier coefficients of the flux linkage are function of direct-and quadrature-axis currents. Hence, they are in the form of two-dimensional look-up tables (LUTs). The used LUTs in the proposed method are independent of rotor position and they are obtained from the finite element analysis (FEA) model. The proposed spatial harmonics compensation method is validated using FEA and experiments for a 3-phase 12/8 MCSRM at generating and motoring modes of operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".