Monitoring voltage stability with real-time dynamics monitoring system (RTDMS®)
Bibliographic record
Abstract
Monitoring and maintaining voltage stability in real-time is extremely important for operating a power system reliably. Inadequate voltage support was a contributing factor in several major blackouts in North America, including the 1996 Western Interconnection and the 2003 North East US /Canada blackout. The RTDMS enables monitoring of voltage stability over a wide area using SynchroPhasor technology. The high resolution data provided by synchrophasor technology is time-synchronized and the RTDMS provides for the wide area visualization of key metrics of the electric power grid across a wide area covering multiple control areas, including visualization using synchronized phasor measurements. The RTDMS application has the capability to monitor voltage stability over a wide area in real-time, enabling operators to quickly identify the location of voltage instability, and based on this information, operators can take corrective actions to prevent voltage collapse conditions. The RTDMS tool monitors the current voltage levels as well as the voltage sensitivity or the rate of change of voltage with respect to power (PV curve sensitivity) at multiple locations and alerts the operators if the voltage deviation or the sensitivity exceeds a set threshold. Additionally, the RTDMS application displays the voltage and angle contour plots for the entire interconnection. This presentation/paper presents the voltage stability monitoring capabilities of the RTDMS tool with illustrations of some practical examples.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".