Securing AGC Systems Against False Data Injection Attacks Using Federated Learning and Model Predictive Control
Bibliographic record
Abstract
The automatic generation control (AGC) system is essential for maintaining power system stability by regulating even minor frequency fluctuations to prevent disruptions and equipment damage. However, cyberattacks targeting communication channels can compromise the AGC system's functionality. False data injection attacks (FDIAs) are a common type of cy-berattack that aims to manipulate the AGC system's behaviour by adding false data to the measurement signals. To tackle the impact of FDIAs on multi-area interconnected power systems, this study proposes a coordinated strategy using federated learning (FL) combined with model predictive control (MPC). The proposed strategy first estimates the true measurement values using the FL approach. Subsequently, MPC utilizes these estimated values to generate mitigation signals, effectively counteracting the FDIAs' impact. The proposed strategy is assessed across various stealthy FDIA scenarios within a three-area interconnected power system. The findings demonstrate the efficacy of the proposed strategy in mitigating the impact of FDIAs on the AGC system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".