A Fixed-Frequency ZVS Integrated Boost Dual Three-Phase Bridge DC–DC LCL-Type Series Resonant Converter
Bibliographic record
Abstract
A new fixed-frequency controlled three-phase dc-dc LCL-type series resonant converter with integrated boost function is proposed for medium to large power applications with wide input voltage variation that is typical of alternate energy sources. The converter includes a dual three-phase LCL-type resonant bridge inverter modules connected in parallel, thus significantly reducing the component stresses when subjected to medium to large power applications. The fixed-frequency control of the output power is achieved by phase shifting the gating signals of one module with respect to the other, while the rectified voltage at the secondary windings of a three-phase high-frequency transformer connected between the two modules is added to the input voltage to boost the supply voltage to the modules. The zero-voltage-switching of all the switches is accomplished by designing the converter to operate in the lagging PF mode for wide variations in the input voltage and the load. Detailed modeling of the three-phase boost section is done and the steady-state analysis of the proposed converter for three-phase LCL-type dc-dc converter modules using complex ac circuit analysis method is presented. For illustration purpose, a dc-dc converter of 600 W is designed, and its performance is verified using PSIM simulations. An experimental model of the converter is built in the laboratory to verify its performance for wide variations in input voltage and load changes.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".