A New Approach to Steady-State Modeling, Analysis, and Design of Power Converters
Bibliographic record
Abstract
Steady-state models of power converters that provide accurate closed-form expressions for converter waveforms are extremely valuable for converter analysis and design, and enable comparative evaluation of different converter topologies. An obstacle in the development of such models is the inherent nonlinearity of switching power converters. This article presents a systematic procedure to model a broad class of power converters using ordinary differential equations (ODEs) with periodic and discontinuous inputs, and provides an approach to determine closed-form expressions for their steady-state waveforms. The formal mathematical proof of the proposed approach to finding closed-form expressions for the steady-state solution of ODEs with periodic and discontinuous inputs, Laplace-based theorem (LBT), is also presented. The presented modeling procedure and LBT, collectively called Laplace-based steady-state modeling (LBSM), serves as an effective analysis and design tool for power converters. The value of LBSM is demonstrated by using it to obtain closed-form expressions for the steady-state waveforms of different types of converters. In particular, two commonly used topologies—the series resonant converter and the phase-shift converter—are analyzed and compared using LBSM and their optimum operating conditions and applications are discussed. The converter waveforms, soft switching ranges and other characteristics obtained using LBSM are also validated through simulations and experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".