Deep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks

• Main Authors: Roberts, Ciaran, Ngo, Sy-Toan, Milesi, Alexandre, Scaglione, Anna, Peisert, Sean, Arnold, Daniel
• Format: Conference Proceeding
• Language: English
• Subjects: Inverters; Libraries; Performance gain; Power systems; Reinforcement learning; Sensitivity; Training
• ISSN: 2378-5861
• DOI: 10.23919/ACC50511.2021.9482815

The deployment of DER with smart-inverter functionality is increasing the controllable assets on power distribution networks and, consequently, the cyber-physical attack surface. Within this work, we consider the use of reinforcement learning as an online controller that adjusts DER Volt/Var and Volt/Watt control logic to mitigate network voltage unbalance. We specifically focus on the case where a network-aware cyber-physical attack has compromised a subset of single-phase DER, causing a large voltage unbalance. We show how deep reinforcement learning successfully learns a policy minimizing the unbalance, both during normal operation and during a cyber-physical attack. In mitigating the attack, the learned stochastic policy operates alongside legacy equipment on the network, i.e. tap-changing transformers, adjusting optimally predefined DER control-logic.