Biblio

With the rapid development of the electric power Internet-of-Things (power IoT) technology and the widespread use of general-purpose software, hardware and network facilities, the power IoT has become more and more open, which makes the traditional power system face new cyber security threats. In order to find the vulnerable device nodes and attack links in the power IoT system, this paper studies a set of attack path calculation methods and vulnerability node discovery algorithms, which can construct a power IoT attack simulation program based on the value of equipment assets and information attributes. What’s more, this paper has carried on the example analysis and verification on the improved IEEE RBTS Bus 2 system. Based on the above research plan, this paper finally developed a set of power IoT attack simulation tool based on distribution electronic stations, which can well find the vulnerable devices in the system.

Plenty of methods applied in system identification, while those based on data-driven are increasingly popular. Usually we ignore the absence of outliers among the system to be modeled, but it is unreachable in reality. To improve the precision of identification towards system with outliers, advantageous approaches with robustness are needed. This study analyzes the superiority of weighted Least Square Support Vector Machine Regression (LS-SVMR) in the field of system identification under random outliers, and compare it with LS-SVMR mainly.