An Average Current Modulation Method for Single-Stage LED Drivers With High Power Factor and Zero Low-Frequency Current Ripple
Bibliographic record
Abstract
Conventional single-stage light-emitting diode (LED) drivers with a high power factor (PF) contain a significant LED current ripple at twice the ac line frequency, and would require large energy storage capacitors to limit the effect on LED light. Conventional designs and novel control techniques aim to power LED loads with a dc voltage to ensure a limited low-frequency LED current ripple. This paper proposes an average current modulation method that is designed to operate in conjunction with single-stage PF correction (PFC) circuits that contain significant ac voltage ripple, while maintaining zero low-frequency current ripple. This allows the energy storage capacitance of the PFC stage to be reduced, avoiding the need for electrolytic-type capacitors and prolonging the life of the LED driver. The average current modulation circuit requires a single low-voltage MOSFET, a current sense resistor, and a simple control circuit. By requiring no additional magnetic components, the cost of the current modulation circuit is very low and has minimal impact on the efficiency of the overall LED driver. Two experimental prototypes, an 8.75-W system with a buck-boost PFC converter and a 25-W system with a flyback PFC converter, have been built to verify the capability and excellent performance of the proposed driving technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".