Deep Reinforcement Learning for DER Cyber-Attack Mitigation

Ciaran Roberts; Sy-Toan Ngo; Alexandre Milesi; Sean Peisert; Daniel Arnold; Shammya Saha; Anna Scaglione; Nathan Johnson; Anton Kocheturov; Dmitriy Fradkin

arXiv · 2009.13088

Deep Reinforcement Learning for DER Cyber-Attack Mitigation

Ciaran Roberts·Sy-Toan Ngo·Alexandre Milesi·Sean Peisert·Daniel Arnold·Shammya Saha·Anna Scaglione·Nathan Johnson·Anton Kocheturov·Dmitriy Fradkin

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Abstract

The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.

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BibTeX RIS

Ciaran Roberts, Sy-Toan Ngo, Alexandre Milesi, Sean Peisert, Daniel Arnold, Shammya Saha, Anna Scaglione, Nathan Johnson, Anton Kocheturov, Dmitriy Fradkin. 2020-09-28. Deep Reinforcement Learning for DER Cyber-Attack Mitigation. https://arxiv.org/abs/2009.13088

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