A novel envelope response technique for large signal dynamic analysis of resonant converters
Bibliographic record
Abstract
In this paper, a novel technique using orthogonal functions to analyze the large signal transitions in resonant converters is proposed. For a given resonant converter, another circuit is constructed with its response orthogonal with that of the original circuit. By combining two circuits into one complex circuit, Laplace transformation and its properties can be applied to derive the envelope response. When there are initial conditions in the resonant converters, orthogonal counterparts of these initial conditions are also investigated and derived. With the above method, large signal dynamic response of resonant converters excited by any external sources and initial conditions can be derived. As examples, series resonant DC/AC converters are investigated by the proposed method under some typical large signal transitions such as startup process with or without initial conditions, transitions when switching frequency, or DC supply voltage source, or load has a step change. General solutions of the envelope response of resonant inductor current are given in detail. The dynamic response as well as the steady state values are therefore obtained. The results are verified by PSPICE simulation. The proposed technique will also be applied to other resonant converters such as parallel resonant DC/AC converters, series resonant DC/DC converters and parallel resonant DC/DC converters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".