Preventing False Tripping Cyberattacks Against Distance Relays: A Deep Learning Approach
Bibliographic record
Abstract
The false tripping of circuit breakers initiated by cyberattacks on protective relays is a growing concern in power systems. This is of high importance because multiple false equipment tripping initiated by coordinated cyberattacks on protective relays can cause large scale disturbance in power systems and potentially lead to cascading failures and blackouts. In this paper, a deep learning based autoencoder is employed to identify anomalous voltage and current data injection to distance protection relays. The autoencoder is first trained with current and voltage data sets representing three-phase faults in zone 1 of a distance relay using a benchmark test system. The autoencoder is then employed to identify anomalies in voltage and current data to prevent false tripping commands by the distance relay. The simulation results verify the capability of the autoencoder model to extract signatures of three-phase faults in the intended zone of a protective relay and detect three-phase fault current and voltage data that do not contain these signatures with high accuracy.
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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".