PMU configuration for system dynamic performance measurement in large, multiarea power systems
Bibliographic record
Abstract
Effective assessment of the dynamic performance of the power system requires wide-area information from properly distributed phasor measurement units (PMUs). However, to maximize the information content of the captured signals, the sensors need to be located appropriately, with due account given to the structural properties underlying the given system. In this paper, two numerical algorithms are proposed to achieve this goal. They aim to maximize the overall sensor response while minimizing the correlation among sensor outputs so as to minimize the redundant information provided by multiple sensors. The sensor responses of interest are the bus voltage magnitude, and the angle and frequency coherency indexes, which are estimated by means of a statistical sampling of power system response signals from a transient-stability program. Through the "successive addition" scheme, one of these algorithms easily incorporates mandatory locations such as tie-line busses and large generator step-up transformers. The proposed approaches are first illustrated on the Hydro-Quebec transmission grid and then on a 9-area/67-bus/23-machine test network designed with well-defined geographical boundaries and pre-specified weak interties between electrically coherent areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".