Dynamic State Estimation-Based Attack Detection Scheme to Supervise Line Current Differential Relays
Bibliographic record
Abstract
Line current differential relays (LCDRs) are commonly used to safeguard high-voltage transmission lines; however, they are intrinsically susceptible to cyber-attacks due to their dependence on communication networks. This study presents how such security gaps may be exploited using false data injection attacks (FDIAs), potentially causing incorrect trip signals. To mitigate this problem, a novel detection method is introduced to distinguish FDIAs from actual internal faults. The approach functions by comparing the locally acquired and the estimated positive-sequence voltages at the relay location. Voltage estimation is performed through two Kalman Filters (KFs), each designed using state-space representations of the transmission line under both normal and faulty conditions, incorporating both local and remote data. Under standard operating conditions, the voltage predicted by one of the KFs corresponds well with the actual measured value, as the model accurately reflects system behavior. However, in the event of an FDIA, both filters deviate from the system's actual dynamics, resulting in noticeable differences between the estimated and measured voltages. Validation of the proposed method on the IEEE 39-bus system through simulations confirms its robustness and accuracy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".