A synchrophasor system emulator - software approach and real-time simulations
Bibliographic record
Abstract
Wide Area Measurement Systems (WAMS) using phasor measurement unit (PMU) demonstrate the great potential in monitoring and analyzing power systems. Synchrophasor applications, such as system monitoring, wide-area controls, linear state estimation, and advanced training simulator, are developed to utilize valuable information from synchronized phasor data. However, there are challenges for both power utilities and application developers in designing, developing, testing and evaluating these applications. One of the main issues is the lack of a real-time simulation platform to emulate the PMU data that may be collected from WAMS. To help address these challenges, this paper proposes a software-based synchrophasor system emulator, ePMU. ePMU emulates synchrophasors by streaming real-time simulation results in standard PMU data format for large power system models. In addition, ePMU emulates actual power system controls by accepting commands from external applications to change network parameters and topologies during real-time simulations. This paper presents this real-time synchrophasors emulator and illustrates its values and benefits.
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.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".