A Modular SiC-Based Step-Up Converter With Soft-Switching-Assisted Networks and Internally Coupled High-Voltage-Gain Modules for Wind Energy System With a Medium-Voltage DC-Grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a fully soft-switched silicon carbide (SiC)-based modular step-up resonant converter with magnetically integrated zero current switching voltage doublers is proposed for medium-voltage (MV) dc conversion in wind energy systems. Conventional step-up resonant dc/dc converters employ either high turns-ratio step-up transformer or step-up resonant circuits to achieve step-up voltage conversion. As a result, they require either complicated expensive transformer structure due to high-voltage electrical isolation requirement or highly voltagegain sensitive step-up resonant circuits, which are not ideal for designing MV converters for wind energy application. In order to solve the aforementioned drawbacks, the proposed converter configuration utilizes both modular step-up resonant circuits and magnetically integrated voltage doublers to achieve the stepup voltage conversion function. The output voltage of each module of the dc/dc step-up converter is regulated through variable frequency control, whereas asymmetrical pulsewidth modulation (APWM) control is utilized to balance all the resonant currents in all the resonant circuit modules in each converter module. Since APWM control is used, a simple passive auxiliary circuit is included in each converter module to extend softswitching operation over a wide range of operating conditions. Simulation results on a 1-kV/28-kV, 5-MW converter system with commercial 1.2-kV SiC MOSFET modules and experimental results on a laboratory-scale 300-V/4.8-kV, 5.6-kW proof-ofconcept prototype with commercial SiC MOSFET and SiC Schottky diodes are provided to validate the theoretical analysis and to highlight the merits of the proposed work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it