High-Voltage Gain Single-Switch Quadratic Semi-SEPIC Converters for Powering High-Voltage Sensors Suitable for Renewable Energy Systems and Industrial Automation with Low Voltage Stresses
Bibliographic record
Abstract
This paper presents two new non-isolated DC-DC converters with and without a coupled inductor based on quadratic voltage conversion. Firstly, the coupled inductor-less type is explained in detail. It employs a voltage-boosting cell and a modified SEPIC structure to provide a high voltage boost ability with a lower and practical value for the switching duty cycle. This allows for lower power loss compared to conventional DC-DC converters. Having only one switch in the proposed converter simplifies the control and reduces the required number of control signals. Furthermore, the presented transformer-less structure can help avoid producing huge voltage spikes across the power switch. In traditional quadratic SEPIC converters, the voltage-boosting cell's capacitor experiences relatively high voltage stress due to the voltage multiplication process. In contrast, the proposed converter offers significantly lower voltage stresses. Hence, it becomes possible to utilize a capacitor with a lower voltage rating, leading to cost savings and improved reliability and availability of suitable components. The first topology can be improved for ultrahigh voltage applications by replacing the middle inductor with a coupled transformer. Consequently, a higher voltage range with a lower switching duty cycle can be attained. Theoretical analysis and mathematical derivations are provided, and the comparison section claims the proposed converter's ability to minimize voltage stress across the switch and output diode. Finally, experimental results are given to verify the effectiveness of the proposed converters at an output power of 260 W.
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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 | 0.001 |
| 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".