Power Losses Estimation on a Semi-Bridgeless PFC Using Response Surface Methodology
Bibliographic record
Abstract
Power losses (Ploss) in Power Factor Corrector circuits (PFC) are hard to predict and estimate due to the changing current and voltages across the power switches. Traditional methods using simplistic conduction and switching loss ( Pcond and Psw) equations leading to inaccurate results. Furthermore, when the information from datasheets is used, the predictions can be severely misleading, as the datasheet only provides information at limited operating condition. In this paper, a technique to estimate Ploss in semi-bridgeless PFCs, based on Response Surface Methodology (RSM), is presented. The proposed methodology extracts the characteristics from each switching device under a wide range of operating conditions and provides results with accuracy well beyond existing methods. As well, the main loss mechanisms for the semi-bridgeless PFC are studied in detail. The device characterization covers all the operating conditions of the semi-bridgeless PFC, thus improving the accuracy of the Ploss prediction. The proposed methodology is validated comparing experimental data against the Ploss estimation calculated using the proposed method and the traditional method.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".