A Decentralized Multi-Agent Reinforcement Learning for Fault Detection and Isolation in Distribution Networks
Why this work is in the frame
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Bibliographic record
Abstract
The increasing integration of renewable energy sources (RES) in modern smart grids introduces challenges in fault detection, isolation, and voltage regulation. Conventional centralized control strategies often face limitations such as communication delays, scalability concerns, and vulnerability to system-wide failures. This paper proposes a fully decentralized multi-agent reinforcement learning (MARL) framework based on the proximal policy optimization (PPO) algorithm to enhance distribution system resilience. The proposed framework develops autonomous MARL agents that independently learn localized control policies for real-time fault detection, isolation, and voltage stabilization. To improve performance, adaptive reward shaping is employed to enhance fault detection accuracy, reduce false positives, and accelerate system recovery. Each PPO agent is trained with customized observation spaces, action strategies, and power flow constraints to ensure robust decision-making under dynamic grid conditions. Simulations on the IEEE 33-bus system demonstrate the proposed framework’s superior fault detection accuracy, faster isolation response, and improved voltage stability compared to conventional reinforcement learning and deep learning models, effectively enhancing grid 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 | 0.001 | 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