A Family of Improved Dual-Buck DC–AC Inverters and Dual-Boost AC–DC Converters
Bibliographic record
Abstract
Conventional cascaded dual-buck dc-ac inverters and dual-boost ac-dc converters can achieve high efficiency by using power MOSFETs without causing reverse recovery issues. In addition, these topologies can provide high reliability by eliminating shoot-through issues. As a result, dead-time in the switching signals can be eliminated and higher switching frequencies can be used. However, these topologies have a major and serious drawback of having more inductors in them and a high magnetic volume. This paper presents a family of improved dual-buck dc-ac inverters and dual-boost ac-dc converters with fewer inductors that can effectively mitigate the high magnetic volume and large number of inductors problems without adding any complexity to the control or hardware. The proposed dualbuck inverters (DBI) and dual-boost converters have all the aforementioned benefits of conventional DBIs and dual-boost converters, respectively. In addition, they reduce the magnetic volume, cost, and complexity of the circuit layout through the use of fewer inductors. To validate the feasibility of the proposed topologies, experimental results of the proposed 2-kW cascaded dual-buck dc-ac inverter with 310 Vdcinput voltage and 420 Vrmsoutput voltage are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".