Application of Feasibility Area for Cybersecurity of Electric Power Systems
Bibliographic record
Abstract
The utilization of digital communication in modern power grids helps deploy the advanced and real-time power system control systems to withstand renewable energy fluctuations. This, however, could open the door to cyberattacks on the communication data. Cybersecurity layers presented by Bad Data Detection (BDD) and pre-occurrence False Data Injection (FDI) detection layers are developed to block these attacks. Such layers, however, cannot guarantee a 100% success rate, and thus, stealthy FDI attacks, which mask the system operation, can penetrate the pre-occurrence layers to change the optimal operation points. To detect the infiltrated stealthy attacks, a new post-occurrence FDI detection layer is proposed in this paper by using the concept of the Feasibility Area (FA), which identifies the region where a power system parameter exits in normal operation conditions using a historical window of data. Deterministic and non-deterministic shapes are used to represent the FA. The state of the power system parameters, such as bus voltage phasors, current phasors, apparent power, and their pattern is utilized to identify the existence of FDI cyberattacks using Pattern Recognition Neural Network (PRNN). The proposed method shows a high performance in detecting stealthy FDI cyberattacks.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".