Three-Level Hysteresis Power Control for Pulse-Density-Modulated Series Resonant Converters
Bibliographic record
Abstract
Modern power conversion electronic devices must not pollute the power distribution system and keep a unit power factor. Furthermore, their design must be size and cost effective. It is also expected that all new designs should have an entirely digital control by 2008. We present a series resonant power converter that is driven by a pulse density modulation (PDM) strategy combined with a three level hysteresis controller to adjust output power. This approach is suitable for a fully digital control design. A single digital signal controller is used to monitor and control the whole system. By the means of simulations and experimental results, it has been shown that a traditional sixteen-level PDM approach helps to maintain a near unity power factor and a low total harmonic distortion. However, such converter can only deliver sixteen output power levels. Design parameters are such that a 25 kHz signal is delivered to the load. Research presented here shows that using the proposed hysteresis control the PDM series resonant converter is able to deliver a wide output power range with 1 W resolution (range from 5 to 360 W). The mean output power error is less than 0.3 %. This strategy can be combined to other PDM approaches to obtain a better power factor and an improved THD within desired power range
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".