Systematic approach to construct and assess power electronic conversion architectures using graph theory and its application in a fuel cell system
Why this work is in the frame
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Bibliographic record
Abstract
With the proliferation of renewable energy generations, power conversion systems (PCSs) are becoming much more complex; it is becoming challenging to search all possible power conversion architectures (PCAs) and find the best optimisation in terms of different objectives. Therefore, this study investigates a systematic approach to construct and evaluate PCAs using graph theory. First, the components in PCSs are graphically modelled as either nodes or edges. Then, a generalised PCA deduction methodology is proposed, and all possible PCAs can be mathematically deduced by modifying elements in adjacency matrices. For a fuel cell (FC) generation system, 45 possible PCAs are found with the proposed method. Furthermore, an evaluation methodology based on graph theory is proposed. The performance indices of the deduced PCAs, including costs, efficiency, and reliability, are calculated. Then, an optimisation approach is applied to finding the best architecture compromise, where the one with the shortest distance to the ideal architecture is considered the best architecture compromise. For the FC demo system, with the proposed assessment methodology, the best architecture compromise (dc‐bus structure) is found among 45 possible architectures. Finally, the experimental platform, which adopts the dc‐bus optimised architecture, is built and experimental results validate the architecture selection.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it