Modelling and Current-Mode Control of a Modular Multilevel DC-DC Converter
Bibliographic record
Abstract
<p>The visions of multi-terminal direct-current (MTDC) grids, DC distribution systems for densely populated urban areas, and DC microgrids for more straightforward integration of distributed energy resources (including renewable energies, electric vehicles, and energy storage devices) have sparked a great deal of research and development in the recent past. An enabling technology towards the fulfilment of these visions is efficient, highly-controllable, and fault-tolerant AC-DC and DC-DC electronic power converters capable of interfacing networks that operate at different voltage levels. This thesis thus presents the results of an in-depth investigation into the operation and control of a particular class of DC-DC converters. The DC-DC converter studied in this thesis is based upon the so-called modular multi-level converter (MMC) configuration, employing halfbridge submodules and with no galvanic isolation. The thesis first presents the governing dynamic and steady-state equations for the converter. Then, based on the developed mathematical model, it identifies suitable variables, strategies, and feedback loops for the regulation of the submodule DC voltages as well as converter power throughput. In particular, two current-control loops are proposed that, in coordination with one another, not only enable the control of the power flow within the converter, but also promise protection against overloads and terminal shorts. The validity of the mathematical model and effectiveness of the proposed control are verified through off-line simulation of a detailed circuit model as well as experiments conducted on a 1-kW experimental setup. The results of this exercise motivate the extension of the proposed control method to more compact designs with galvanic isolation and enhanced power handing capabilities.</p>
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".