A Remotely Control Dimming System for LED Lamps with Power Factor Correction
Bibliographic record
Abstract
Modern street lighting systems require an energy-efficient dimmable road lighting system with even light distribution for safety and energy saving purposes. Existing standard TRIAC-based dimmers introduce power quality issues especially for a large-scale lighting network. This paper proposes a dimming control technique for Light Emitting Diode (LED) lamps, while maintaining high voltage and current quality. Dimming function is achieved by connecting a Voltage Source Converter (VSC) dimmer system between the AC supply and the lighting load. The VSC dimmer system will achieve high power factor and low current harmonic distortion when dimming the LED lamps. The VSC system is a comprehensive solution for most of power quality problems in the network. A VSC dimmer system prototype of 500 VA 120V has been built. An advanced feature is added to the VSC dimmer system to remotely send/receive messages between the system and the user through a Graphical User Interface (GUI). Specifically, the user can communicate with the VSC dimmer system by using Raspberry Pi. Experimental results and power analysis comparison between utilizing the TRIAC-based dimmer and the VSC dimmer system for dimming function are discussed. Setting a dimming profile to endorse energy saving will be discussed as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".