Self-Healing Q-Learning Power Routing Protocol for Smart Grids
Bibliographic record
Abstract
The Internet of Energy (IoE) paradigm introduces new challenges and opportunities for efficient power routing in decentralized energy networks. A novel approach is implemented to enhance power routing efficiency within the IoE framework by integrating adaptive Q-learning techniques with a maintenance phase. The proposed distributed Self-Healing Protocol (SHP) utilizes discovered paths during the maintenance phase instead of recomputing a path to transmit a Power Packet (PP) in case of a fault. Our proposed Self-Healing Q-Learning Routing protocol aims to optimize energy delivery by dynamically adapting routing decisions and addressing system maintenance in real-time. This power routing protocol operates without centralized control, leveraging autonomous behaviors for system resilience and performance optimization. It covers all scenarios, including Multiple Source Single Load (MSSL), Multiple Source Multiple Load (MSML), and Single Source Multiple Load (SSML) scenarios. This approach is validated using MATLAB simulations.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".