Modelling And Simulation of Shunt Active Power Filter For Power Quality Improvement
Bibliographic record
Abstract
Nowadays, power electronics devices are mostly used everywhere like domestic application, industry utilization and commercial application throughout the world [1-2]. Most of the power electronics devices are non-linear load such as variable frequency drives (VFD), personal computers, arc furnaces, switch mode power supplies (SMPS), converters and so on which led to power quality problems like voltage sag, voltage fluctuation, noises, flickering and so on [3-4]. When the current waveform does not follow the voltage waveform, it is referred as a non-linear load. Insulation failures, electrical device heating, power losses, interface problems in communication systems, and worst-case electrical power system failures are all caused by harmonic distortions on the distribution side. [5]. Therefore, eliminating the power quality issues are a big challenge for both utility and customer [6]. Furthermore, current-related power quality issues are mostly caused by harmonics, inadequate reactive power, and unbalanced loads [7]. SAPF is considered to be one of the most effective methods for achieving higher levels of power quality. By generating equal and opposite magnitude of harmonic current at PCC, SAPF is employed to inject compensating current and reduce the harmonics [8]. While performing the adopted approach, it should be noted that, the THD should not exceed 5%, as adopted by IEEE standard [9].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".