Implementation of a Dynamic Voltage Restorer System Based on Discrete Wavelet Transforms
Bibliographic record
Abstract
This paper presents an implementation of the discrete wavelet transform (DWT) using passiveLCfilters for operating a dynamic voltage restorer (DVR) system. The proposed implementation is based on designing Butterworth passiveLCfilters with cutoff frequencies that are identical to cutoff frequencies of DWT associated digital filters. These passiveLCfilters can detect abnormal conditions that may disrupt the quality of the power supplied to sensitive loads in a power system. Detecting any abnormal condition is realized through extracting high- and low-frequency components present in system voltages using high-pass and low-pass filters, respectively. The designed Butterworth passiveLCpassive filters are third-order systems to simplify their practical implementation as well as their integration with the test power system and the DVR. Simulation and experimental test results for transient voltage dip and steady-state harmonic distortion cases show significant performance improvement of the DVR system operated by the designed Butterworth passiveLCfilters. The proposed DWT-operated DVR system using Butterworth passiveLCfilters is implemented and tested for improving the power quality under different abnormal 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".