A Modified Valley Fill Electronic Ballast Having a Current Source Resonant Inverter With Improved Line-Current Total Harmonic Distortion (THD), High Power Factor, and Low Lamp Crest Factor
Bibliographic record
Abstract
In this paper, a modified valley fill (VF) circuit is employed to combine with a current-fed resonant inverter as a passive high power factor (PF) electronic ballast. A conventional VF circuit limits the line current to conduct when the conduction angles are: 30deg les omegat les 150deg and 210deg les omegat les 330deg during the line period, which results in high total harmonic distortion (THD). The modified VF circuit has the following advantage: When the capacitors are connected in parallel, the voltage across the capacitors is one-third of the peak voltage, which allows the conduction angle of the line current to be further extended to 19.5deg les omegat les 160.5deg and 199.5deg les omegat les 340.5deg, so that a lower THD can be achieved. The high lamp crest factor (CF) problem generated by the high ripple voltage from the modified VF circuit is improved in the proposed ballast as variable frequency control is employed to continuously regulate the lamp current. An experimental prototype is then built in the laboratory to verify the feasibility of the proposed work for a 26-W compact fluorescent lamp. The final results confirm that a PF of 0.986 and a lamp CF of 1.49 are achieved with the proposed circuit, whereas a PF of 0.96 is achieved with the conventional VF ballast.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".