On-Line Coherency Recognition and Identification of the Reduced Order System Using Phasor Measurement Units
Bibliographic record
Abstract
Phasor measurement units are increasingly being deployed in modern power systems for smart management of the networks. One of important application of such units is to check the system stability conditions. Traditional methods for transient stability assessments have gained little attention in power industry, as they need right modeling of the large-scale power networks with nonlinear models for generators and loads, which are not readily available. In this paper, a new technique is presented for on-line coherency recognition and identification of the reduced order system. In the proposed method, the only requirement is to have access to some measurement data from Phasor measurement units. The new method involves: coherency recognition, model order reduction and parameter estimation. Finally, time domain simulation of the reduced order system is used for the transient stability assessment. Simulation results show that identified reduced order model by the proposed method is valid for transient stability assessment even if the operating conditions and system configurations and parameters are not available or change, as it only uses the on-line measurement data. The result in the proposed algorithm is independent of the kind of disturbance and its location in power system
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".