Mitigation of Cyber-Attacks on Wide-Area Under-Frequency Load-Shedding Schemes
Bibliographic record
Abstract
This paper investigates the problem of cyber-attacks on Wide-Area Under-Frequency Load Shedding (WAUFLS), as these schemes are critical to maintaining power systems stability. First, we perform a detailed analysis on existing WAUFLS schemes and show that an adversary can launch a False Data Injection (FDI) cyberattack by manipulating the frequency measurements or Power Flow Measurements (PFMs), which may lead to system losses, unnecessary shedding of important loads, and system-wide blackout. Second, to address this vulnerability, we propose a novelReliableStates WAUFLS(RSLS) scheme to protect against FDI cyber-attacks. The disturbance calculation and load shedding process in RSLS are based on reliable system states, obtained using a proposed data-classification method on the PFMs that secures the state estimation operation. These reliable states are then used to perform the power flow in order to calculate the power mismatch. The calculated magnitude of disturbance, as well as the obtained system states, are used to decide on the amount and locations of the loadshedding. We validate the effectiveness and accuracy of RSLS by conducting extensive simulation on the IEEE-39 bus New England system using PSCAD/EMTDC. The results confirm the proposed scheme’s capabilities in evaluating system disturbance and performing load-shedding, thus protecting the system during under-frequency conditions, and demonstrate its robustness against FDI 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".