Preventing False Tripping Cyberattacks Against Distance Relays: A Deep Learning Approach

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.