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

Abstract

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