PWM Control of a Dual Inverter Drive using a Floating Capacitor Inverter
Bibliographic record
Abstract
A carrier-based pwm scheme is presented for a dual inverter drive operating an open winding induction machine. One inverter is supplied from a dc power source while the other uses a floating capacitor dc voltage source. The output voltages of the two inverters have a nominal 90° phase shift relative to each other and low quality pwm motor voltages is obtained when using "standard" pwm techniques. Two pwm controllers are compared: 1R2C uses a single reference and a dual carrier to control a switch; 2R2C uses both a dual carrier and a dual reference signal (phase-difference and phase-average). The latter scheme allows for independent control of the two inverter output voltages while simultaneously coordinating their pwm switching patterns to obtain high quality motor pwm voltages. Independent inverter voltage control allows a field-oriented controller to be used for a fast motor speed response, whilst allowing decoupled control of the floating inverter dc voltage. The 2R2C pwm scheme is shown to produce a 5-level "effective line voltage" and is compared with several alternatives using either a single or dual inverter drive. The quality of the pwm waveforms is demonstrated using simulations, load current THDF, and the line voltage harmonic volt-seconds. Results are experimentally validated using a DSP digital controller.
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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) | 0.003 | 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 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".