Graph Multi-Agent Reinforcement Learning for Inverter-Based Active Voltage Control
Bibliographic record
Abstract
Active voltage control (AVC) is a widely-used technique to improve voltage quality essential in the emerging active distribution networks (ADNs). However, the voltage fluctuation caused by intermittent renewable energy makes it difficult for traditional voltage control methods to deal with. In this paper, the voltage control problem is formulated as a decentralized partial observable Markov decision process (Dec-POMDP), and a multi-agent reinforcement learning (MARL) algorithm is developed considering each controllable device as an agent. The new formulation aims to adjust the strategies of agents to stabilize the voltage within the specified range and reduce the network loss. To better represent the mutual interaction between the agents, a graph convolutional network (GCN) is introduced. By aggregating the information of adjacent agents, complex latent features are effectively extracted by the GCN, hence promotes the generation of voltage control strategy for the agents. Meanwhile, a barrier function is applied in MARL to ensure the system voltage within a safe operation range. Comparative studies are conducted with traditional voltage control and other MARL methods on IEEE 33-bus and 141-bus systems, which demonstrate the performance of the proposed approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".