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Volt-VAR and Volt-Watt control functions are mechanisms that are included indistributed energy resource (DER) power electronic inverters to mitigateexcessively high or low voltages in distribution systems. In the event that asubset of DER have had their Volt-VAR and Volt-Watt settings compromised aspart of a cyber-attack, we propose a mechanism to control the remaining set ofnon-compromised DER to ameliorate large oscillations in system voltages andlarge voltage imbalances in real time. To do so, we construct control policiesfor individual non-compromised DER, directly searching the policy space usingan Adam-based augmented random search (ARS). In this paper we show that,compared to previous efforts aimed at training policies for DER cybersecurityusing deep reinforcement learning (DRL), the proposed approach is able to learnoptimal (and sometimes linear) policies an order of magnitude faster thanconventional DRL techniques (e.g., Proximal Policy Optimization).

Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation