A Method for Supply Voltage Boosting in an Open-Ended Induction Machine Using a Dual Inverter System With a Floating Capacitor Bridge
Bibliographic record
Abstract
An operational approach to an induction machine is presented that uses an open winding connected to a dual inverter system. A floating capacitor inverter bridge boosts the fundamental voltage available to the machine and arbitrarily sets the operating power factor of the main inverter bridge connected to the dc battery power source. During operation, the motor current charges the floating bridge dc capacitor voltage to a naturally stable dc voltage level and the ac voltage delivered to the machine is the resultant sum of the two inverter bridge voltages. Machine voltage boosting is then achieved by adjusting the fundamental phase angle difference between the two inverters to control the charge stored in the floating bridge capacitors. With the floating bridge providing reactive voltage support and therefore boosting the available supply voltage to the induction machine, there are two main outcomes: minimization of the supply current required for operation beyond the base speed of the electric machine, and supply voltage regulation of the drive system. Experimental results are used to verify the operation of the floating bridge arrangement by examining the load power factor angle and the phase difference between the two bridges. Results are presented for a passive RL load to illustrate the supply current reduction at high fundamental frequency operation, and a modified 2-hp, 1800-r/min induction to illustrate the dc voltage supply droop compensation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".