Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids

The distributed generation capabilities of microgrids (MGs) arise as essential assets in enhancing grid resilience. integration energy sources, controllable loads, and prosumers necessitates the deployment potent control communication synergies. While those synergies transform MGs into cyber-physical systems through information technologies able to sense, control, actuate local resources they inadvertently expose cyber-attack threats. Increasing security critical against “black swan” events, i.e., high-impact low-probability incidents, is a major priority for MG operations. Such if left unabated, can intensify elicit system dynamics instability, eventually causing outages failures. In this article, we develop an integrated approach multiagent perform detection malicious attacks based on subspace methods. We employ small-signal model autonomous/islanded consider different attack models targeting MG’s secondary frequency control. detector constructed via identifying stable kernel representation autonomous attack-free case. illustrate impact well feasibility developed method simulation Canadian urban benchmark distribution system.