High-Efficiency Operation of an Open-Ended Winding Induction Motor Using Constant Power Factor Control
Bibliographic record
Abstract
A controller is presented for an open-ended winding motor dual-inverter drive (DID), where main and floating inverters are supplied from a dc power source and a floating dc capacitor, respectively. The controller utilizes the efficiency characteristic of induction machines, where high power conversion efficiencies are obtained when operating the machine with a constant fundamental power factor, typically around 0.70-0.75, over a wide load range and under variable frequency. The controller uses the drive's topology to maintain the motor's desired power factor. In essence, the main inverter's output voltage is used to control the floating inverter's dc capacitor voltage to keep the injected fundamental voltages of both inverters at a desired ratio. The floating inverter's voltage is operated with a 90° lead relative to the main inverter and uses a constant maximum amplitude modulation depth to minimize the capacitor's operating voltage. This approach updates the motor's voltage automatically to ensure constant power factor operation and improves the floating capacitor's voltage stability during transient conditions. The inherent voltage-boosting capability of this topology is especially beneficial in extending the constant torque region of the motor and improving performance in the speed range extension region. Simulation and experimental results verify the predicted motor efficiency gains and stability under speed and load transients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".