Synchrophasor Big Data Architectures, Platforms and Applications: A Review
Bibliographic record
Abstract
The world is moving towards an era of data driven analytics and decision making. Concurrently, the electrical power industry is moving towards a data driven analytical environment from a model driven analytical environment. Electrical power industry utilizes different types of data. Synchrophasor data is one of the main data types associated with many of the power system applications. However, with the expansion of Phasor Measurement Units (PMU) networks, the synchrophasor data is becoming a Big Data (BD) issue. Therefore, many researchers have drawn their attention on synchrophasor big data handling and utilization. This paper briefly discusses power system BD architectures and standard architectures available in real-world applications. The goals of this paper are to make a review of existing BD architectures and commercially available platforms for synchrophasor applications; to do a comparative analysis of existing BD architectures; and to do a review of the existing applications and the compatibility these applications with the existing BD platforms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".