Isolated Soft Switching Current Fed LCC-T Resonant DC–DC Converter for PV/Fuel Cell Applications
Bibliographic record
Abstract
This paper proposes an “isolated soft switching current fed LCC-T resonant dc-dc converter for PV/fuel cell applications.” This converter is able to achieve zero voltage switching for front-end inverter switches and zero-current switching for voltage doubler diodes. The majority of dc-dc converters have compact capacitive (C) filter in output, but high harmonic currents through transformer makes them bulky. The proposed converter attempts to improve harmonic content through a transformer under wide load and input variations as well as it can significantly reduce required turns ratio of transformer to achieve required output voltage. For the above reasons, the proposed converter can offer better footprints and therefore it can be a potential candidate for PV/fuel cell applications. This paper presents basic operating principal, detailed analysis, control, and design of the proposed converter with the help of an example. This converter operates in two modes, i.e., constant duty cycle mode and constant frequency mode, depending on load demand, and is explained through operating curves. Finally, experimental results are included for justifying the proposed theory.
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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".