A Novel Attack Identification Mechanism in IoT-Based Converter-Composed DC Grids

This article proposes a novel attack identification mechanism for Internet of Things-based (IoT-based) converter-composed dc grids, where each agent collects its own and neighbors’ measurement data output regulation to meet preceding power-sharing consensus. Independent from model-free or average-model-based detection theories, this is mainly inspired by converter stitching behavior analysis. Correspondingly, when facing latent signal substitution instigation attacks, through comparing estimated signals with received ones source authentication, both self-sensors neighbors will be inspected. Eventually, not only can such attacks detected but also respective sources identified. A simulation case 4-agent 800-V IoT-based grid on Simulink hardware 3-agent 90-V the dSpace testing platform were investigated. Experimental results revealed that estimation ratio error kept lower than 3.9% all successfully identified, verifying effectiveness proposed mechanism.