A hybrid current controller for a 1-phase PWM rectifier combining hysteresis and carrier-based schemes to achieve a zero current error and unipolar PWM waveforms
Bibliographic record
Abstract
A current controller is described for 1-phase PWM rectifiers that combines both the functionality of hysteresis-based and carrier based controllers. The development of this current controller is described with the aid of generic PWM controllers that use a sinusoidal modulating signal and a signal integrator. These controllers mimic the operation of the 1-phase PWM rectifier and are used to determine the optimal gain-constant associated the current-error feedback signal. The resultant current controller is relatively insensitive to variations in rectifier parameters such as the supply inductance and the dc link voltage magnitude. The controller combines the excellent current waveshaping ability of hysteresis-based controllers together with the constant switching frequency of carrier-based controllers. The main controller features include: real-time generation of the sinusoidal amplitude modulation signal from the PWM signal; elimination of phase-shifts caused by the limited rate-of-change of the rectifier current; a near zero current-error over a PWM half-cycle; no skewing effects in the current-error signal. Simulation results, using a per-unit system of values, are used to illustrate the step-by-step development of the current controller. A DSP-based controller is used, together with an IGBT rectifier, to demonstrate the experimental operation of the 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 | 0.001 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".