Innovative Three and Four-Output-Port Bridge (TOPB and FOPB) for Series and Parallel Multilevel Inverters: Graph-Theoretic Analysis, Modulation Techniques, and Their Application
Bibliographic record
Abstract
This article presents a novel three-output-port bridge (TOPB) for multilevel inverters (MLIs), designed to optimize the interaction between input sources in both series and parallel configurations. Unlike conventional multilevel converters, which feature two output ports supporting only series interactions, the proposed TOPB includes three output ports, two of which interface with conventional MLIs or a power switch to enable parallel interactions alongside series connections. A four-output-port bridge (FOPB) serves as an intersection cell for two TOPBs, facilitating the extension to a higher number of voltage levels. A rigorous mathematical analysis compares the structural properties of the cascaded TOPB (CTOPB) and cascaded H-bridge (CHB) topologies. By modeling both configurations as graphs, we evaluate connectivity metrics, including adjacency matrices, Laplacian matrices, and algebraic connectivity. The results demonstrate that CTOPB-MLI exhibits superior connectivity, attributed to its three-output-port architecture. This freedom offers valuable insights for single-source, multisource multilevel converters, and store-and-forward energy MLI. Furthermore, a one-dimensional space vector modulation (OD-SVM) technique is introduced to efficiently control modular converters using predefined switching tables. This approach enhances performance and addresses challenges associated with conventional modulation techniques, particularly in managing noncomplementary switching signals. The CTOPB topology’s performance is validated through comprehensive simulation and experimental results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".