Control and Operation of Dynamic Voltage Restorer With Online Regulated DC-Link Capacitor in Microgrid System
Bibliographic record
Abstract
This article presents a dynamic voltage restorer (DVR) topology based on the adaptive noise canceling (ANC) technique, which can be used for both voltage compensation and harmonic mitigation. Furthermore, this article aims to investigate the DVR performance when installed in a microgrid (MG) during both normal operation of the utility and during utility disturbances. One of the main objectives of this article is to reduce the cost of inverter-based DVR by reducing both the size of the dc-link capacitor and rating of switching elements. The voltage of the dc-link capacitor is regulated to low voltage level using a transformer and a pulsewidth modulation (PWM) rectifier to achieve both effective voltage regulation and drawing a sinusoidal line current from the grid and thus not to contribute to the increase of the THD of the utility current. Furthermore, the voltage across the switches can be limited to low value by an adequate design of dc-link capacitor size, capacitor voltage, and sag level to be compensated. Finally, the effectiveness and fast response of the proposed DVR for the compensation of voltage disturbances and current harmonics is confirmed by simulation using MATLAB/Simulink during the steady-state and transient operations to analyze the performance of the scheme under different operating conditions.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".