Detection of false data injection in automatic generation control systems using Kalman filter
Bibliographic record
Abstract
Automatic Generation Control (AGC) is a vital power system component responsible for the system frequency regulation. Also, it helps to minimize the tie-line power deviation in multi-area systems. The AGC uses communication to send/receive measurements/control actions about frequency and power deviation in power system. Small errors in AGC can drive the frequency out of the allowable range and blackouts may occur. Since communication links in recent smart grids are targets of cyber attackers, this renders AGC systems in modern smart grids susceptible to false data injection attacks. This paper investigates the impact of cyber attacks on the AGC and how the adversary can perform an attack against it. Also, it proposes a method to detect these attacks using a Kalman filter-based technique. To confirm the effectiveness of this approach, a 2-area power system is simulated using MATLAB/Simulink. The results show that the utilized technique is capable of detecting various types of false data injection attacks against AGC systems.
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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".