A Scalable FMI-Compatible Cosimulation Platform for Synchrophasor Network Studies
Bibliographic record
Abstract
Rigorous evaluation of a synchrophasor network in terms of communication network quality, cybersecurity, and phasor data concentrator (PDC) performance is presented. To this aim, a cosimulation platform is designed based on the multiagent environment for complex system cosimulation platform. The proposed platform is characterized by particular features including scalability (no limitation in terms of nodes/buses of communication/power systems), compatibility with the functional mock-up interface standard, and capability of synchronized integration of any two abstract simulators (in this article, NS-3 for communication network and MATLAB/Simulink for power system). The simulations are performed for the detailed model of the IEEE-34 bus distribution network including phasor measurement units and the PDC. The simulations include (1) evaluation of widely used synchrophasor communication technologies (i.e., user datagram protocol and transmission control protocol), (2) evaluation of the PDC performance under both relative and absolute wait time logics, and (3) assessment of cybersecurity vulnerabilities of the synchrophasor network. The proposed platform can be utilized for implementation and evaluation of communication technologies/protocols of synchrophasor networks in terms of bandwidth, packet missing, latency, and cybersecurity vulnerabilities. Data pushing mechanism of the PDC can be also evaluated using the proposed platform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".