Smart Grid Data Denoising and Compression Using Wavelet Packet Transform
Bibliographic record
Abstract
The efficient operation of the Smart Grid is contingent on the accurate analysis of power signals, which are often compromised by disturbances.These power signals, captured by quality monitors, generate substantial volumes of data, thereby necessitating effective compression strategies to facilitate manageable data transfer and collection.Besides mitigating the costs associated with data storage, transmission, and encryption, these compression techniques must ensure minimal reconstruction error to avoid distortion in the original signal.Moreover, it becomes imperative to eliminate noise for the attainment of high-quality signals, critical for disturbance detection.In this paper, a novel method has been developed employing lower-order wavelets (Db3, Db2, Db2, Db2, and Db1).This method decomposes the signal from the first to fifth level utilizing wavelet Packet Transform, testing the efficacy on Phasor Measurement Unit data.Simulation results demonstrate enhanced data compression and noise reduction compared to previous designs, with the signal being approximately reconstructed.This innovative approach offers a facile, efficient, economical, and time-saving solution for smart grid data management, marking a significant advancement in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".