Resilient and Online Reconfiguration of Distribution Systems into Multi-Islanded Microgrids
Bibliographic record
Abstract
Despite the increasing deployment of microgrids, distribution networks remain vulnerable to upstream failures, which can lead to widespread blackouts. This vulnerability arises from the fixed boundaries and limited adaptability of microgrids in dynamic conditions. To address this, a BreadthFirst Search (BFS)-based algorithm is proposed to identify optimal reconfiguration strategies, enabling the transformation of disconnected distribution systems into multiple self-sustained microgrids while minimizing dependence on remote switches. To achieve real-time operation, the solution is embedded in a Deep Reinforcement learning framework that learns adaptive switching policies online. A custom reward function prioritizes supply restoration and minimizes load shedding, while an exponential epsilon-greedy strategy balances exploration and exploitation during training. Simulation results on the IEEE 33bus distribution system show that the proposed method improves convergence speed, decision efficiency, and resilience, outperforming conventional learning models. The framework enables adaptive multi microgrids reconfiguration for enhanced system resilience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".