A Single Switch Split DC-Rail Boost Converter Topology Operated Under Steady-State Unbalanced Load Conditions in Discontinuous Conduction
Bibliographic record
Abstract
A single switch split dc-rail boost converter topology is described that uses a mutually coupled two winding inductor to balance the voltage drop across both of its capacitor smoothed split dc-rail input and output voltage terminals. The operation of the converter topology is described in discontinuous conduction with an unbalanced output load in steady-state. The resultant converter topology obtains balanced input and output voltages under extreme unbalanced load conditions without requiring extra converter control features. A current sharing action is described between the two coupled windings during both the switch on-time and off-time. This action is responsible for the voltage balancing feature of the converter when the converter load is unbalanced between the two output dc voltage rails. Simulations and experimental results are used to illustrate the nature of the winding current waveforms that produce this current sharing action. Load imbalances exceeding 3:1 can easily be compensated for, and simulations and experimental results show that typical deviations of the converter input and output voltages can be limited to within the range 1% to 8% for load imbalances of up to 2:1. The voltage imbalance experienced in the two dc rail output voltages is subject to design choices and linked to the winding leakage inductance and dc voltage gain.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".