Circuit-averaged and state-space-averaged-value modeling of second-order flyback converter in CCM and DCM including conduction losses
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Bibliographic record
Abstract
Modeling and analysis of basic DC-DC converters is essential for enabling power-electronic solutions for the future energy systems and applications. Many average-value modeling (AVM) techniques including state space averaging and circuit averaging have been developed over the years and available in the literature. Average-value modeling of ideal PWM converters neglects parasitics (losses) to simplify the derivations and modeling procedure. In this paper, we consider a second-order flyback converter with conduction losses parasitics. We propose two new AVMs using the circuit averaging and state space averaging approaches, respectively. By taking into account conduction losses, the accuracy of proposed average-value models improves. The new models are verified for large signal time-domain transients and small-signal frequency-domain analysis in continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The derived (corrected) models show noticeable improvement over the traditional (un-corrected) models.
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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 | 0.000 | 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 | 0.001 |
| 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 it