Adaptive Reinforcement-Learning Based Automatic Generation Control for Smart Grid Cyber-Resilience
Bibliographic record
Abstract
Adaptive control in smart grids, which dynamically adjusts control parameters in real time to respond to changing grid conditions, has become a growing research focus due to the increasing variability and uncertainty in modern grid operations. Beyond optimizing system performance, adaptive control can play a crucial role in mitigating malicious cyberattacks that exploit vulnerabilities in smart grid infrastructure, thereby strengthening cyber resilience. In this paper, we investigate a Reinforcement Learning (RL)-based adaptive PI control approach for centralized Automatic Generation Control (AGC), for the purposes of detecting and rejecting False Data Injection (FDI) cyberattacks manipulating frequency and tie-line power AGC measurements. We compare the performance of our RL-based method against traditional PI AGC, showing improved resilience under various attack scenarios that manipulate AGC measurements in a three-area power system.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 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".