Resilient and Online Reconfiguration of Distribution Systems into Multi-Islanded Microgrids
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
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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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