An Isolated Soft-Switched DC-DC Converter For Interconnection Of Medium- And Low-Voltage DC Grids
Bibliographic record
Abstract
As the electric power grid increasingly hosts energy storage devices, renewable energy resources, plug-in hybrid and electric vehicles, and data centers, it is expected to benefit in the future from a multi-layer DC structure meshed within its legacy AC architecture. As such a multi-layer grid structure evolves, interconnection of DC grids with different voltage levels will become necessary. For such interconnections and for power-flow control, efficient isolated DC-DC converters are a key enabling technology. This thesis thus presents the results of an in-depth investigation into the operation, modulation, control, and performance assessment of a particular DC-DC converter configuration. The proposed DC-DC converter, which is based upon a hybrid combination of the conventional dual-active-bridge topology and the modular multi-level converter (MMC) configuration, is a potential candidate topology for interconnection of medium- and low-voltage DC grids. The thesis first introduces the circuit topology and presents the basics of operation and governing steady-state equations for the converter. Then, based on the developed mathematical model, it identifies a suitable modulation strategy for the converter bridges and submodules, as well as strategies for the regulation of the MMC submodule capacitor voltages and soft switching of the constituent semiconductor devices. The proposed converter topology offers significant benefits including galvanic isolation, utilization of the transformer’s leakage inductance, soft switching for high-frequency operation, and bidirectional power flow capability. The validity of the mathematical model, effectiveness of the proposed modulation and control strategies, and the realization of soft switching are verified through off-line simulation of a detailed circuit model as well as experiments conducted on a 1-kW experimental setup.
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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 | 0.001 | 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".