A small signal state space model of single stage three level resonant AC/DC converters
Bibliographic record
Abstract
This paper presents a small signal state space modeling approach for a single stage three level resonant power factor correction converter operating with either variable frequency phase shift modulation or variable frequency asymmetrical pulse width modulation control. Modeling is achieved using a combined averaging and multiple-frequency approach. The model gives good prediction of both the transient and steady state operations of the converter. The dynamics of the output filter can also be represented as well as the influence of parasitic parameters such as the capacitor equivalent series resistance (ESR). This method also allows the separation of both the frequency and duty cycle as the control variables. All state variables are broken down into their frequency components, including high and low frequency components (the high frequency components being in the order of the switching frequency and the low frequency components being in the order of power line frequency) and the amplitudes of the frequency components are used as the new state variables. This can then be linearized, thus a small signal model is obtained. The model has the switching frequency as well as the required duty ratio for the pulse- width or phase shift modulation as separated control input variables, which facilitates a better and more accurate controller design. The developed model is verified analytically and experimentally on a 2.3 kW, 48 V, input voltage 90-265 V RMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".