Visible to the public Deep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks

TitleDeep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks
Publication TypeConference Paper
Year of Publication2021
AuthorsRoberts, Ciaran, Ngo, Sy-Toan, Milesi, Alexandre, Scaglione, Anna, Peisert, Sean, Arnold, Daniel
Conference Name2021 American Control Conference (ACC)
Keywordsattack surface, Inverters, Libraries, Metrics, Performance gain, Power systems, pubcrawl, reinforcement learning, resilience, Resiliency, Scalability, Sensitivity, Training
AbstractThe 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.
Citation Keyroberts_deep_2021