A New Three-Port DC–DC–AC Converter With Single-Phase Buck–Boost AC Output
Bibliographic record
Abstract
This paper presents an innovative topology of a three-port DC-DC-AC converter, along with its modulation strategy, aimed at seamlessly integrating DC energy sources and storage systems within (hybrid) DC and AC microgrids or feeding DC and AC loads. The converter combines the functions of two separate circuits (DC-DC and DC-AC) into a unified topology, reducing component count, implementation costs, conversion stages, and related power losses. Compared to counterparts, this new circuit requires only five switches, fully shared between the DC and AC ports; offers a broad range of buck-boost voltage operations, making it ideal for connecting with series-connected DC sources, such as a string photovoltaic system, where the input voltage may vary both above and below the DC and AC output levels; all three ports of the proposed converter support bidirectional current flow, enabling battery bank charging, while this bidirectional flow in the DC and AC ports allows for power sharing between the two microgrids, acting as an interlink converter; and incorporates 2ndorder LC filters at each port, ensuring a continuous and low-ripple current flow for both the DC and AC ports. This paper presents a comprehensive analysis of the topology, modulation method, operational performance, and comparative assessments, all of which are validated by experimental tests, achieving a peak efficiency of 96.6%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".