Adaptive Sliding Mode Based Loss-Free Resistor for Power-Factor Correction Application
Bibliographic record
Abstract
The emulation of virtual resistance finds various applications in the power processing industry. This paper proposes a novel notion of adaptive sliding mode based loss free resistor (ASLFR). This is achieved by allowing the input power of the power-out power-in (POPI) system to vary, in order to accommodate the load demands. In this paper, the concept is illustrated for power-factor correction (PFC) applications. The ASLFR is used to achieve the dual purpose of harmonics-free rectification along with excellent system response under load and line transients. The scheme serves itself as an efficient single-stage PFC solution. A generic mathematical formulation of the scheme is presented, which can be used for different converters. Then on, a boost topology, operating in continuous conduction mode, is chosen to demonstrate theoretical developments and to showcase the effectiveness of the scheme. The robustness of the proposed controller to any line or load variation is established. A fast voltage recovery with almost no undershoot/overshoot is achieved at transients by using the proposed controller. Additionally, a qualitative analysis is provided to demonstrate the expediency of the proposed ASLFR. The theoretical claims are well supported by simulation as well as experimental results.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".