Real Time Dynamics Monitoring System (RTDMS®) for use with SynchroPhasor technology in power systems
Bibliographic record
Abstract
This paper describes the use of Real Time Dynamics Monitoring System (RTDMS®) for use with SynchroPhasor System Technology (SPST). Analysis of some major power blackouts in US and other countries have shown that monitoring power system status and dynamic transient events in real-time can enable the operators to identify deteriorating system conditions early and help them take preventative actions to avoid or reduce likelihood of blackouts. Using RTDMS to monitor the power system with SynchroPhasor technology is expected to improve reliability, provide wide area visibility over multiple control areas, and provide system operators with real-time measurement of key grid metrics, including phase angles, oscillation modes, energy and modal damping. These grid dynamics indicators cannot be obtained in real-time from the existing historic tools such as SCADA or State Estimators. The SPST has great potential for enhancing power system stability and increasing power transmission system capability through high speed response based controls. Given, the proper visualization and analysis tools, this technology can assist operators in avoiding major system disturbances like the North American blackouts that occurred in Western US on August 10, 1996 and in Northeast US and Canada on August 14, 2003. The paper will outline the use of RTDMS with SPST and describes how it enhances the visibility of the power system. The RTDMS is in use at several ISOs and utility locations in North America.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".