A Coupled-Inductor-Based Buck–Boost AC–DC Converter With Balanced DC Output Voltages
Bibliographic record
Abstract
With the development of distributed power generation sources and the widely used dc characterized loads, the dc nanogrid becomes more and more attractive and the converters with three terminal outputs are increasingly studied. Considering the costs, the efficiency, and also the safety, the grounding configuration needs to be addressed when designing the ac-dc converter for a dc nanogrid system. An ac-dc converter with three terminal outputs has been presented for the united grounding configuration based dc nanogrid. Nevertheless, for this type of converters, the output voltages are unbalanced in case of unequal dc loads. This paper proposes a novel buck-boost ac-dc converter with the capacity of output voltages self-balancing by using a coupled inductor. The operation of this converter will be presented in details through analyzing its equivalent circuits. The small signal model of the system in different working modes is given, and the whole system control diagram shows how to balance the dc output voltages. A 220 V/50 Hz/1.5 kW prototype has been developed. Experiments are carried out to verify the effectiveness of the coupled-inductor-based converter.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".