A Cooperative Multiagent Framework for Self-Healing Mechanisms in Distribution Systems
Bibliographic record
Abstract
Because of society's full dependence on electricity and high cost of system outages, one important goal is to increase the reliability of the power system, which means that a salient attractive feature of smart grid is its self-healing ability. Smart grids will develop and enhance the automation of distribution by operating in a distributed manner through new digital technologies such as monitoring, automatic control, two-way communication, and data management. In this work, the smart grid concept and technologies have been applied to construct a self-healing framework for use in smart distribution systems. The proposed multiagent system is designed to locate and isolate faults, then decide and implement the switching operations to restore the out-of-service loads. The proposed control structure has two layers: zone and feeder. The function of zone agents in the first layer is monitoring, making simple calculations, and implementing control actions. Feeder agents in the second layer are assigned to negotiation. The constraints include voltage limits, line current limits, and radial topology. Load variation has been taken into consideration to avoid the need for further reconfigurations during the restoration period. The results of the simulation conducted using the new framework demonstrate the effectiveness of the proposed control structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".