Smart Grid Data Compression of Power Quality Events using Wavelet Transform
Bibliographic record
Abstract
Smart grids typically incorporate a communication networking layer onto the electric power grid to exchange the data and the information between the intelligent electronic devices and the supervisory control and data acquisition (SCADA). The smart grid monitoring and control requires the acquisition and the transmission of a large volume of data over such communication networks. This process sets new requirements for the existing data communication networks within the smart grid regarding the network transmission capacity and the data storage. Data compression is an effective mean to compress the power quality (PQ) data and hence saving the cost of data storage while meeting the communication network transmission capacity limits. The previous work on PQ data compression uses wavelets multiresolution signal decomposition into various decomposition levels. Thresholding is then applied on each wavelet decomposition level to capture the features needed for signal reconstruction. The goodness of compression is significantly affected by the choice of the wavelet basic function as well as the choice of the number of decomposition levels. This paper looks into identifying the most suitable wavelet basic function and the suitable number of wavelet decomposition level to achieve high compression of PQ disturbances. The study considered 80 wavelets and 4 categories of PQ disturbances such as sag, swell, interruption and notches. The results have been presented and the conclusions were drawn.
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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 | 0.002 |
| 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".