Multi-Agent Distributed Deep Learning Algorithm to Detect Cyber-Attacks in Distance Relays

Abstract

Distance relays are critical components in protection systems of power grids that can be attacked by cyber-attackers. Indeed, a cyber-attacker injects fake data into a distance relay to pretend a fault has happened, and the distance relay must be tripped. Thus, a new powerful approach, named Multi-Agent Distributed Deep Learning (MADDL) method is proposed to tackle cyber-attacks in distance relays. Unlike centralized methods, the protection system with several distance relays is mapped to a multi-agent distributed system by employing the graph theory, in which the distance relays are considered as the agents of the multi-agent system or the nodes of the considered graph. Each agent is only connected to the neighboring agents to exchange voltage and current data. Then, a deep neural network as a cyber-attack detection structure is assumed for each agent that utilizes the local voltage and current data and the received data from the neighboring agents to detect the attacks. Hence, the considered detection structures are tuned by employing train data, obtained by simulating the grid in different types of faults. Then, the tuned detection structures are evaluated by a test dataset, including data from the grid under various faults and the normal situation by injecting fake data as cyber-attacks. The developed method has been employed for three different case studies, including IEEE 6-bus, IEEE 14-bus, and IEEE 118-bus power grids. According to the simulation results, the proposed algorithm has succeeded in identifying more than 99.88% of faults and cyber-attacks.