Characterization of Commercial LED Lamps for Power Quality Studies
Bibliographic record
Abstract
Light emitting diode (LED) lamps exhibit nonlinear characteristics causing a negative influence on the power grid and on public health. High harmonic contents injected by LEDs as well as their sensitivity to voltage fluctuations in the network should be further examined. This article aims at achieving a better understanding of the characterization of commercial dimmable LED lamps and their impact on power quality (PQ) parameters. A testbed has been implemented as a platform to investigate the current and voltage quality issues by conducting various experimental tests that include PQ concerns such as harmonic analysis, voltage flickering, voltage sag, and voltage swell. The experimental setup allows the execution of light intensity, harmonic current contents, and voltage measurement of commercial LED lamps. Data processed using the MATLAB software tool have been used to analyze the results. The percent flicker is used to evaluate varying degrees of flickering happening in LED lighting networks. The effect of changing the light intensity for dimming purposes on the perceptibility of flicker has been studied and discussed as well. This article provides a solid reference for researchers working on the PQ improvement of LED lamps.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".