Testing the Frame-Angle-Based Direct Torque Control for $3\phi$ Induction Motor Drives
Bibliographic record
Abstract
This article presents the performance evaluation of the frame-angle-based (FAB) direct torque controller (DTC) for 3φ induction motor drives (IMDs), which are fed by 3 six-pulse wavelet-modulated (WM) dc-ac power electronic converters (PECs). The tested DTC is designed to adjust the de- and qe-axis components of the voltages (vedsand veqs) applied to a 3 IMD. The adjustments in vedsand veqsare created in response to changes in the load torque, command torque, drive speed, and/or system parameters. Desired adjustments in vedsand veqsare set by regulating the angle ϑ of the frame created by vedsand veqs. This frame produces reference modulating signals that are required to generate WM switching pulses for operating the 3 dc-ac PEC in the IMD. The complete IMD system, incorporating the FAB-DTC, is implemented for a 10-hp IMD system for performance evaluation. Tests of the FAB-DTC for the 10-hp IMD are conducted for various changes in the load torque, command torque, drive speed, and system parameters. Simulation and experimental test results demonstrate fast, accurate, reliable, and dynamic responses with minor sensitivity to variations in system parameters.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".