Transformerless single-phase UPQC for large scale LED lighting networks
Bibliographic record
Abstract
The paper presents a comprehensive Power Quality (PQ) solution to improve grid current harmonics and light intensity flickers in large scale LED lighting networks. Low cost and low power LED lamps exhibit current harmonic contents due to their nonlinear characteristics. A large scale lighting network requires tens to hundreds LED lamps installation, the resultant harmonic currents pollute the grid seriously. Furthermore, Light intensity fluctuations are becoming a concern nowadays to many users, as safety and health problems. This phenomenon is mainly caused by heavy loads as they lead to voltage fluctuations and deteriorating in PQ and hence visual flickering in LED lamps. This paper proposes a single phase transformerless half-bridge unified power quality conditioner (UPQC) topology with its controls to mitigate most of PQ problems in a network. An active power filter injects harmonic currents and reactive current to provide unity power factor and a dynamic voltage restorer quickly supports the load voltage for any voltage dip, swell or flickering in the network. The performance of the proposed UPQC has been verified by simulations, and small signal models have been determined. A 500VA/120V prototype has been built to verify the models. Experimental results, steady-state characteristics and stability study are given in the paper. The experimental results have a good agreement with the theoretical findings.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".