A distributed autonomous approach for bulk power system restoration by means of multi‐agent system
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Bibliographic record
Abstract
Abstract In recent years, the electric utility industry worldwide has been facing pressure to be deregulated. Also, the risk of blackout in large areas will increase. Actually, it is still vivid in our memory that the northeastern United States 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 12 generating units and 12 loads, and three remote areas with 12 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. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(1): 69–76, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20485
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it