Multi-Agent Approach to Bulk Power System Restoration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, the electric utility industry worldwide has been facing pressure to be deregulated. Along with it, risk of blackout in large area will also increase. Actually, it is still vivid in our memory that the northeastern US and southern Canada suffered the worst blackout in history. Consequently, a method to find the optimal solution rapidly is needed all the more. In this paper, we propose a new multi-agent method for a bulk power system restoration. In order to demonstrate the capability of the proposed multi-agent system, it has been applied to a model bulk power system, which consists of three local areas including twelve generating units and twelve loads, and three remote areas with twelve loads. A large number of simulations are carried out on this model network with changing conditions. The simulation results show that the proposed multi-agent approach is effective and promising.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it