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<p>This paper proposes a novel attack identification mechanism for internet-of-things-based (IoT-based) converter-composed DC grids, where each agent collects its own and neighbours' measurement data for output regulation to meet a preceding power-sharing consensus. Independent from model-free 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. </p>
<p>The hosting capacity determines the grid ability to host integrated installations, where mutual-limitations among points of connection (POCs) are important and formulate a combinational feasible region. Intended to assess such interaction for region determination, this paper proposes a multidimensional hosting capacity region derivation scheme in a radial grid. As this scheme can be designed conservative, it earns more industrial acceptance from a risk-averse perspective. This multidimensional region not only exploits grid power delivery potential, but also benefits for making grid congestion management decisions. A simulative 10.5kV dutch grid case has been tested accordingly. Corresponding results revealed that the region conservative property is guaranteed. In the given case study, compared to the original hosting capacity concept, the region hypervolume gain ratio keeps higher than 1.94. With proper measure selection, the estimated region occupation ratio can keep up to 92.50% and the congestion management decision computation time can be reduced by 58.0% in respect to that in the monolithic model. Both the effectiveness of proposed scheme and the concept benefit in grid congestion management are successfully verified. </p>
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.
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