A Double-Loop Primary-Side Control Structure for HB-LED Power Regulation
Bibliographic record
Abstract
This paper presents a study of high-brightness LED (HB-LED) strings under power drive control, in which the LED's input power is controlled rather than its current. In the conventional constant current drive technique, any changes in the LED string's forward voltage results in output light variations due to a strong dependence between the LED output light to its input power. Under constant power drive control, the LED string's forward voltage variations due to ambient temperature changes and aging are compensated. To evaluate the power drive control method, an inner-outer-loop control structure is developed by utilizing an ac-dc flyback converter. To this end, a primary-side LED power estimator and controller are proposed to achieve simultaneous output power regulation and input power factor correction. An expression is obtained for the LED power in terms of its input current and ambient temperature that can provide qualitative and quantitative behavior in constant-current and constant-power drive regimes. Experimental results using a Cree CR22-32L LED string are presented and compared with the constant current drive technique for temperature variations in the range 25-80°C. For the aforementioned temperature range and using similar hardware, the proposed controller results in an 8% reduction of the LED output light when compared to a 13% reduction using the constant current drive 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 | 0.000 |
| 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 | 0.000 |
| 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".