Real-Time Processing and Quality Improvement of Synchrophasor Data
Bibliographic record
Abstract
This paper proposes a real-time algorithm for processing and quality improvement of synchrophasor data (SD). The proposed algorithm first recovers the missing SD reported by phasor measurement units (PMUs), and performs low-rank approximation on data streams. Then, the enhanced data stream can be redirected toward various power system applications. The nonconvex matrix completion (MC) method with Schatten-q quasi-norm (lq) penalty is used to recover the missing SD in realtime. Unlike most MC methods which have been developed for batch data processing, the proposed method is able to perform fast recovery of streaming data even for high reporting rates of PMU data. The low-rank approximation method is used to suppress the noise of the streaming SD, and to efficiently compress the batch data for archiving. Real-life PMU data as well as simulation data are used to evaluate the performance of the proposed algorithms. The results obtained using both real experimental and simulation SD confirm that the proposed SD processing framework significantly improves the quality of data, particularly during transient conditions and in noisy environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".