Inrush Current Limit or Extreme Startup Response for LLC Converters Using Average Geometric Control
Bibliographic record
Abstract
LLC resonant converters suffer from a startup inrush current that may push power switches beyond the safe operating area. The conventional method of limiting the startup inrush current is to adopt the frequency decrement method; however, doing so, results in the overdesign of magnetic components and the gate driver circuit, sluggish startup response, and circulating current in light and no-load conditions. To tackle these problems, this paper proposes a nonlinear controller called the average geometric controller (AGC), offering low-cost implementation requirements. The proposed controller uses the advantages of a new LLC average large-signal model, in order to analyze the large-signal nature of the converter. Studying the average nature of LLC converters enables the use of low-bandwidth sensors, and lower sampling rates, while improving the system performance. In addition to limiting the startup inrush current without employing a very high switching frequency, the proposed AGC provides an extreme dynamic startup response for LLC converters, with near zero voltage overshoot. In order to validate the theoretical analysis, experimental results of an LLC converter are presented using the proposed controller. Comparative experimental analysis is performed with a conventional method for limiting inrush current. Moreover, the accuracy of the average large-signal model is experimentally validated.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".