DRL2FC: An Attack-Resilient Controller for Automatic Generation Control Based on Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based controller designed to improve the resilience of automatic generation control systems in power grids against cyberattacks, specifically false data injection attacks, by dynamically adjusting generator setpoints.

Contribution

It presents a novel DRL-based AGC controller that adapts to cyber threats and load changes, enhancing power grid security and stability.

Findings

The DRL controller effectively mitigates cyberattack impacts.

Experimental results show improved grid stability under attack.

The approach outperforms traditional control methods.

Abstract

Power grids heavily rely on Automatic Generation Control (AGC) systems to maintain grid stability by balancing generation and demand. However, the increasing digitization and interconnection of power grid infrastructure expose AGC systems to new vulnerabilities, particularly from cyberattacks such as false data injection attacks (FDIAs). These attacks aim at manipulating sensor measurements and control signals by injecting tampered data into the communication mediums. As such, it is necessary to develop innovative approaches that enhance the resilience of AGC systems. This paper addresses this challenge by exploring the potential of deep reinforcement learning (DRL) to enhancing the resilience of AGC systems against FDIAs. To this end, a DRL-based controller is proposed that dynamically adjusts generator setpoints in response to both load fluctuations and potential cyber threats. The controller learns these optimal control policies by interacting with a simulated power system environment that incorporates the AGC dynamics under cyberattacks. The extensive experiments on test power systems subjected to various FDIAs demonstrate the effectiveness of the presented approach in mitigating the impact of cyberattacks.