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Open Access Publications from the University of California

Deep reinforcement learning for der cyber-attack mitigation

  • Author(s): Roberts, C
  • Ngo, ST
  • Milesi, A
  • Peisert, S
  • Arnold, D
  • Saha, S
  • Scaglione, A
  • Johnson, N
  • Kocheturov, A
  • Fradkin, D
  • et al.

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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