Design and Implementation of Novel Noninverting Buck–Boost AC–AC Converter for DVR Applications
Bibliographic record
Abstract
This article proposes the new noninverting buck–boost ac–ac converter, which can minimize the reverse recovery loss by disabling the body diodes of switches and using the external fast recovery diodes for its current freewheeling. In particular, the proposed ac–ac converter takes advantages of metal-oxide semiconductor field effect transistors and external fast recovery diodes to achieve the high efficiency. Also, it has the high reliability without the shoot-through and dead-time considerations in the circuit. Moreover, it can be simply implemented without employing the conventional commutation control. This results in decreasing the control complexity. As its proper application, a new type of dynamic voltage restorer (DVR) based on the proposed noninverting buck–boost converter is described. It enables to solve the voltage sag and swell problems by utilizing both positive and negative gains of converter. Then, the hardware prototype of proposed ac–ac converter and DVR are implemented to verify their practical effectiveness.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".