Power factor control for high efficiency operation of an open-ended winding motor using a dual inverter drive with a floating bridge
Bibliographic record
Abstract
A novel control scheme is presented for an open-ended winding motor dual inverter drive, where a primary inverter is supplied from a dc power source and a secondary inverter is supplied from a floating dc capacitor with no power source. Examination of the basic single phase equivalent circuit model of an induction machine reveals that near optimal motor efficiency is achieved at a relatively constant motor fundamental power factor over a wide range of motor loads and drive operating frequencies, e.g. 0.71 for the motor used in this work. The drive control described uses the phase difference between the fundamental output voltages of the primary and secondary converters in order to control the motor's terminal voltage and to maintain a motor power factor of 0.71. This control is maintained over the full range of the motor's load and drive frequency settings. The floating capacitor bridge voltage is regulated using PI feedback control, with the capacitor voltage error signal as the input and the amplitude modulation index of the primary inverter as the output. The capacitor voltage reference signal is related to the per-unit drive frequency and measured current: as the motor's current or frequency changes, the capacitor's voltage reference is updated in a proportional manner to both parameters. This control approach keeps the fundamental phase difference between the two inverters within a stable operating range, maintains the power factor of the main bridge close to unity, and improves the floating bridge's dc capacitor voltage stability during transient load changes. The drive control performance is presented using detailed simulations and experimental testing; verifying the regulation of the floating capacitor's voltage as well as the predicted efficiency gains for the induction motor under constant power factor operation as compared with conventional drive control.
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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 | 0.001 |
| 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".