Real-Time Implementation and Testing of a Wavelet-Controlled Dynamic Voltage Restorer
Bibliographic record
Abstract
This paper presents an experimental testing of a discrete wavelet transform (DWT)-controlled dynamic voltage restorer (DVR) system for power quality improvement. The proposed DWT-operated DVR system is designed to continuously detect, diagnose and respond to different transient disturbances that may affect the power quality. The input voltage to the proposed DVR system is adjusted by approximation signals to ensure transient-free inputs. Also, the output of the proposed DVR is obtained using a dc-ac inverter, whose switching signals are controlled by a detail signal. Both approximation and detail signals are obtained using DWT of the line voltage. An experimental setup for the proposed DWT-controlled DVR system is constructed, where the DWT is implemented using a dSPACE ds1102 digital signal processing board. Moreover, sinusoidal pulse-width modulation switching signals for the output-end dc-ac inverter are generated using the same DSP board. In this paper, the proposed DVR system is experimentally tested for transient voltage dip, steady-state under voltage and transient harmonic distortion cases. Test results for these cases show accurate, fast and effective DVR system responses. In all tested cases, the load voltage is maintained at its predefined nominal value during and post any abnormal conditions, which validate the proposed operating strategy
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".