Data-driven decision system for adaptive control of FACTS devices in the New York State grid
Bibliographic record
Abstract
The combination of growth of electricity demand, restrictions on transmission system expansion, and grid security/reliability requirements gives the development and application of local controllers such as Flexible AC Transmission Systems (FACTS) devices, Power System stabilizers (PSS), and High Voltage Direct Current (HVDC) systems a high priority. In the last decades, significant number of these type of controllers have become fully operational to achieve different functionalities and improve the flexibility and controllability of the grid. However, design and application of a system-level controller that dynamically changes the set points of the local devices based on the grid conditions, still needs lots of nurturing. This paper concerns with the enhanced utilization of system-wide controllable resources in the New York State grid as the system conditions vary. To achieve the greatest benefit we propose a data-driven decision system for grid management, which offers the application of a wide-area system awareness platform for adaptive utilization of the system-wide controllable resources. The solution is broad in nature but as a starting point, in this paper, the attention is given to dynamically changing the set points of shunt-connected FACTS devices in order to minimize system violations following a major disturbance. The effectiveness of the proposed solution has been verified on a unique system that uses the real data of the New York State grid. All the optimizations are based on the NETSSWorks software (New Electricity Transmission Software Solutions Works) that is explained in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".