A Novel Highly Efficient Torque-Sharing Algorithm for Dual Stator Winding Induction Machines for Various Speed Regions
Bibliographic record
Abstract
This paper proposes a novel and highly efficient torque-sharing algorithm for DSWIMs. This algorithm introduces three modes of operation for DSWIMs based on the command speed and the required load power. Implementing this algorithm makes the accurate flux calculation and control possible in very low speed regions, including zero speed. In higher speed regions, if one of the winding sets is able to supply the required power solely, the other is switched off to augment the overall efficiency. In higher required powers, both winding sets cooperate to supply the load torque and the required electromagnetic torque division between the two winding sets is fulfilled based on their power ratings. This guarantees for overloading avoidance in various operation conditions. Moreover, the optimal flux condition is guaranteed in various speed regions free of the torque -sharing. In addition, this algorithm is general and can be implemented in direct torque control and field-oriented control schemes in various reference frames. The proposed algorithm has been experimentally implemented in a 3.3kW vector controlled DSWIM drive system. In this flux and speed control system, the flux is controlled such that a search based Maximum Torque per Ampere (MTPA) algorithm is realized. The proposed MTPA strategy is insensitive to DSWIM parameters and the load variations. The experimental results confirm the functionality of the proposed DSWIM drive system in various operation conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".