An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation
Bibliographic record
Abstract
The ever-increasing penetration of centralized and distributed renewable energy, power electronics-based transmission equipment and loads, advanced protection and control systems, storage devices and new power market rules all contribute to the growing dynamics and stochastic behaviors being observed in today's grid operation. Understanding operational risks and providing prompt control actions are of great importance to ensure secure and economic operation of a bulk power system. In this paper, a novel integrated online dynamic security assessment system (DSAS) is developed that intakes real-time EMS snapshots combining both bus/branch and node/breaker network models, performs dynamic contingency analysis under various conditions, calculates real-time transfer limits, and provides online control suggestions to mitigate operational risks. Several unique and innovative features are developed to address practical challenges, including: (1) real-time stitching of power flow information from both node/breaker and bus/branch models covering different geographical regions of interest; (2) online corrections and enhancements to power flow and dynamic models; (3) equipment-name-based modelling approaches for complex contingencies and control systems; and (5) distributed computing capabilities for significant computational speed enhancement. The developed DSAS has been deployed in the control center of State Grid Jiangsu Electric Power Company with hundreds of HVAC and 8 (U)HVDC transmission lines, which has been running reliably since Sept. 2018, achieving satisfactory performance in improving situational awareness and economic operation of the power grid.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".