On Cognate Multiport Converters through Graphbased Generalized Duality
Bibliographic record
Abstract
Featured with a more complicated configuration, the modelling, analysis, and derivation of multiport converters (MPCs) requires much more effort than conventional two-port converters. The duality principles from circuit theory and graph theory can serve well as a powerful tool to deal with these challenges. However, a fundamental property of duality has been missing in the power electronics community for over 40 years, i.e., different dual MPC topologies can come from the same original MPC, even for those with planar circuits. And this indeed limits our understanding of the MPCs. To fill this gap, the missing theoretical foundations are provided in this work, forming the generalized duality principles for systematic modelling, analysis, and derivations of MPCs. The theoretical foundations are firstly presented through advanced concepts in graph theory. Then, extensive MPCs are selected as examples to validate the feasibility of this theory. It is shown that a 3-port non-isolated MPC can have 8 different duals (for MPCs with more ports, this number will go even higher) and these duals are related to each other by common electrical relationships. Therefore, their modelling, analysis, and operation design can be achieved in a systematic way.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".