A Remotely Central Dimming System for a Large-Scale LED Lighting Network Providing High Quality Voltage and Current
Bibliographic record
Abstract
Standard TRIAC-based dimmers introduce power quality issues especially for a large-scale lighting network. Other existing dimming protocols involve additional wiring systems and/or additional controllers to light emitting diode (LED) drivers. This paper proposes a central dimming system for a large penetration of LED lamps. The dimming system is remotely controlled through a webpage or a desktop application. Dimming is achieved while maintaining high voltage and current quality waveforms, which results in a high power factor and a low input current harmonic distortion. The system does not require additional wiring or specific adjustments to commercial dimmable LED drivers. The system allows scheduling a dimming profile to endorse energy saving. In the proposed dimming system, dimming function is achieved by connecting a voltage source converter (VSC) between the grid and the LED lamps. An advanced feature is added to the VSC dimmer to remotely send/receive messages between the system and the user through a graphical user interface. Thus, the user can communicate with the VSC dimmer by sending commands and receiving feedback information. The influence of communication delay on system stability is analyzed by using small signal models. A VSC dimmer prototype (500 VA/120 V) has been built with a communication module to provide remote control. Experimental results and comparisons between utilizing the TRIAC-based dimmer and the VSC dimmer for dimming function are discussed in the paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".