False Data Injection Attacks on Wide-Area Out-of-Step Prediction Scheme
Bibliographic record
Abstract
Predicting out-of-step (OOS) conditions in power systems is crucial for preventing electrical and mechanical damage to generators and breakers. One effective approach utilizes wide-area measurement systems to predicts OOS conditions by analyzing the voltage angle difference at both ends of a tie-line, calculating slip frequency and acceleration, and tracking the trajectory on a slip-acceleration plane. However, this method’s reliance on communication networks makes it vulnerable to cyber-attacks, such as false data injection attacks (FDIAs). This paper introduces an FDIA model to illustrate how attackers can infiltrate the system and mislead operators in detecting power swings and OOS conditions. The model generates an attack signal and injects it into a measured voltage angle to deceive OOS prediction and power swing detection schemes. The paper evaluates the model’s performance and its impact on the OOS prediction scheme using the IEEE 39-bus test system under both OOS and stable power swing (SPS) conditions. Index Terms-Out-of-step prediction, wide-area measurement systems, cyber security, false data injection attacks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".