A Step-Up Reconfigurable Multimode <i>LLC</i> Converter Module With Extended High-Efficiency Range for Wide Voltage Gain Application in Medium Voltage DC Grid Systems
Bibliographic record
Abstract
In this article, a newmodular multimode reconfigurable step-up resonant converter that is capable of extending very high efficiency from full load to reduced load conditions is proposed for wide voltage gain application in medium voltage dc (MVdc) grid system. The proposed converter module is able to switch from a hybrid control scheme that consists of variable frequency and phase shift control to pulsewidth modulation (PWM) control in the auxiliary switch of the output voltage quadrupler (VQ). In addition, the input bridge can also be reconfigured to a half-bridge mode to suit high input voltage range while utilizing a hybrid control technique that consists of variable frequency and asymmetrical PWM control. With the proposed approach, the converter module is able to achieve constant output voltage regulation without requiring a wide spectrum of switching frequency or phase shift control. Hence, close-to resonance operation with very high efficiency can be maintained for a wide range of input voltage and loading conditions. Soft switching operations are always guaranteed for all the primary side switches, the output diodes and the auxiliary switch in the VQ. The steady-state and dynamic performance of the proposed modular multimode step-up converter are validated through simulation results on a silicon carbide (SiC) based 360 V–1 kV/16 kV, 80-kW system and experimental results on a proof-of-concept 150–400 V/6.6 kV, 10-kW SiC laboratory prototype. Results confirmed that the efficiency is maintained between 97.8% and 99.1% from full load to at least 20% load condition for the specified input voltage range.
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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) | 0.002 | 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 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".