Bipolar Ripple Cancellation Method to Achieve Single-Stage Electrolytic-Capacitor-Less High-Power LED Driver
Bibliographic record
Abstract
Conventional topologies for high-power LED drivers with high power factors (PFs) require large capacitances to limit the low frequency (100 or 120 Hz) LED current ripples. Electrolytic capacitors are commonly used because they are the only capacitors with sufficient energy density to accommodate high-power applications. However, the short life span of electrolytic capacitors significantly reduces the life span of the entire LED lighting fixture, which is undesirable. This paper proposes a bipolar (ac) ripple cancellation method with two different full-bridge power structures to cancel the low-frequency ac ripple in the LED current and minimize the output capacitance requirement, enabling the use of long-life film capacitors. Compared with the existing technologies, the proposed circuit achieves zero double-line-frequency current ripple through LED lamps and achieves a high PF and high efficiency. A 100-W (150 V/0.7 A) LED driver prototype was built which demonstrates that the proposed method can achieve the same double-line-frequency LED current ripple with only 44-μF film capacitors, compared with the 4700-μF electrolytic capacitors required in the conventional single-stage LED drivers. Meanwhile, the proposed prototype has achieved a peak power efficiency of 92.5%, benefiting from active clamp technology.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".