Novel high-gain hybrid current-driven DC-DC converter topology
Bibliographic record
Abstract
A novel high gain current-driven DC-DC converter topology is proposed in this paper. This circuit is aimed for applications which require wide input and output voltage ranges, such as PV micro-inverters, electric vehicle battery chargers etc. As compared to the resonant converters, the size of inductor can be reduced by an order of magnitude and resonant capacitors eliminated, by the use of current-driven topology. These advantages result in increased power density and higher power conversion efficiency of the converter. A major limitation of the current-driven topology is that it can provide a maximum gain of just over unity. Further, this value of maximum gain also depends on the parasitic capacitances of the converter components. These issues make the converter operation more susceptible to component parasitics (such as the transformer inter-turn capacitance), resulting in degraded converter performance. The high-gain current-driven topology, presented in this paper overcomes the disadvantages of the conventional current-driven converter topology, by the introduction of a parallel capacitor at the high voltage side of the transformer winding. Introduction of parallel capacitor provides a two-fold advantage to the conventional current-driven topology. First it makes the effect of parasitic capacitor small so that the control system is not much sensitive to it, and it provides a high gain due to pre-charging of the network inductances at the beginning of each switching cycle. Simulation and experimental results validate the viability of proposed converter topology for applications requiring wide range of input and output voltages.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".