Ultrahigh Step-Up Cubic Semi-SEPIC Converter
Bibliographic record
Abstract
In this paper, a novel cubic converter based on a modified single-ended primary-inductor capacitor (SEPIC) topology is presented. The proposed cubic semi-SEPIC converter (C³SSC) employs a single switch, simplifying control, improving efficiency, and reducing overall complexity. This converter is designed to draw a continuous current from the DC source, making it an ideal choice for renewable energy applications, where stable system is critical. The current through the inductors decrease toward the output stage. This feature minimizes both power and thermal losses, enhancing efficiency in high-power applications. A key advantage of the proposed converter is its ultra-high voltage boost capability, achieved without the need for a coupled transformer. This design not only enables a significant voltage gain with a low to medium switching duty cycle but also mitigates common issues such as leakage inductance and voltage spikes on switches, which are often encountered in transformer-based designs. The converter's wide duty cycle control range, spanning from 0 to 1, allows for precise operation across various conditions. This flexibility enables the achievement of high voltage gain with a steep slope in the voltage gain versus duty cycle characteristic, enhancing the converter's adaptability and efficiency. A small-scale prototype of the converter has been built and tested in the laboratory, demonstrating its performance with an output power of 550 W and an output voltage of 400 V from a 24 V input and 55% switching duty cycle. This makes it particularly suitable for DC microgrids powered by renewable energy sources, where compact, efficient, and high-gain converters are essential.
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) | 0.004 | 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".