Linearized DC-MMC Models for Control Design Accounting for Multifrequency Power Transfer Mechanisms
Bibliographic record
Abstract
The dc-modular multilevel converter (DC-MMC) is one of a new class of single-stage modular multilevel dc-dc converters that has recently emerged for high-voltage dc applications. This paper presents the first small-signal state-space model for the DC-MMC that is able to account for the multifrequency power transfer mechanisms within the converter. Derived from a dynamic phasor model representation of the DC-MMC, the developed model is linear time-invariant (LTI), allowing for the application of conventional LTI tools for both analysis and design. The small-signal dynamics are validated by simulation results from a full switched model demonstrating its accuracy. A simplified model derived from the full LTI system is presented that readers can utilize to develop dynamic controls for the DC-MMC. As a case study, this benchmark model is leveraged to propose a dynamic controller that regulates dc power transfer between networks and balances the capacitor voltages. Control block diagrams are also provided that enable systematic control design of the DC-MMC via standard linear methods. Case study simulations verify the efficacy of the developed controls for dc network applications. The presented small-signal modeling and control design methodology can be readily applied to any MMC-based topology.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".