Feedback Modelling of Passively Balanced Flying Capacitor Multilevel Converters
Bibliographic record
Abstract
This work demystifies the fundamental principles of passive balancing of flying capacitor multilevel (FCML) converters by developing a feedback modelling method that is applied to natural balancing and coupled inductor balancing. Coupled inductors are a robust method of passively and inexpensively balancing large-order FCML converters. The feedback models are used to relate the role of loss and coupling ratio in the strength of passive balancing methods, thus showing how coupled inductors are superior to natural balancing when tightly coupled inductors are designed with a high quality factor. The feedback models are used to predict the steady-state imbalance of coupled inductor FCML converters under the influence of external disturbances and compare them to experimental results. A four-phase, five-level FCML converter is used to verify the feedback models and show the limitations of coupled inductor balancing at singular points.
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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) | 0.000 | 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 | 0.000 |
| 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".