Online Maximum Torque per Ampere Control for Doubly-Fed Induction Machines
Bibliographic record
Abstract
This paper introduces a control approach to optimize the ratio of the torque to the total input current in windings of the doubly fed induction machine (DFIM). Essentially, this strategy aims to share the currents more evenly between stator and rotor windings to achieve a specific torque level. Due to constraints imposed by the flux and the frame alignment, the angles of rotor and stator currents are interrelated. Consequently, a fundamental relationship is established between these angles, so the total current magnitude in terms of the rotor current angle is expressed. The optimal angle required to implement the maximum torque per total Ampere (MTPTA) control strategy is then determined using a numerical minimization process. Additionally, the maximum torque per inverter Ampere (MTPIA) strategy is proposed in this paper, which is demonstrated by minimizing the rotor current magnitude while maintaining a constant rotor current angle for a given torque level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".