Synchrophasor Big Data Architectures, Platforms and Applications: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it