A TRIAC Dimmable Single-Switch Electronic Ballast With Power Factor Correction and Lamp Power Regulation
Bibliographic record
Abstract
With the global concerns on the issue of how to conserve the limited energy resources, compact fluorescent lamps (CFLs) have been forced in a lot of countries to replace the power-hungry incandescent lamps to reduce the energy cost. However, conventional passive power factor corrected electronic ballasts used in CFLs suffer two major drawbacks: 1) they are unable to achieve close-to-unity power factor and 2) they are unable to provide the same amount of light intensity under fluctuating line voltage. This paper proposed a simple lamp power regulation method for a very high-power factor single-switch electronic ballast, which allows the electronic ballast to maintain almost constant lamp power at its full-power condition for a wide range of the input voltage by controlling the dc-link voltage of the ballast power circuit through duty ratio control. When a TRIAC phase-cut dimmer is used to dim the CFL, the dc-link voltage regulation control loop is deactivated; so that the lamp power decreases accordingly during dimming. The analysis and operating principles of the controller are discussed in this paper. MATLAB, PSIM simulation, and experimental results are provided to highlight the merits of the proposed work.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".