Visible to the public Reliability analysis of power grids with cyber vulnerability in SCADA system

TitleReliability analysis of power grids with cyber vulnerability in SCADA system
Publication TypeConference Paper
Year of Publication2014
AuthorsYichi Zhang, Yingmeng Xiang, Lingfeng Wang
Conference NamePES General Meeting | Conference Exposition, 2014 IEEE
Date PublishedJuly
KeywordsBayes methods, Bayesian attack graph model, critical data eavesdropping, cyber security, cyber vulnerability, cyber-physical power systems, FOR model, forced outage rate, Generators, LOLP, loss of load probability, power engineering computing, power grid reliability analysis, power grids, power system reliability, power system security, reliability, reliability test system 79, remote access point, RTS79, SCADA system, SCADA systems, Substations, supervisory control and data acquisition system, system breaker, system breaker trip
Abstract

As information and communication networks are highly interconnected with the power grid, cyber security of the supervisory control and data acquisition (SCADA) system has become a critical issue in the power system. By intruding into the SCADA system via the remote access points, the attackers are able to eavesdrop critical data and reconfigure devices to trip the system breakers. The cyber attacks are able to impact the reliability of the power system through the SCADA system. In this paper, six cyber attack scenarios in the SCADA system are considered. A Bayesian attack graph model is used to evaluate the probabilities of successful cyber attacks on the SCADA system, which will result in breaker trips. A forced outage rate (FOR) model is proposed considering the frequencies of successful attacks on the generators and transmission lines. With increased FOR values resulted from the cyber attacks, the loss of load probabilities (LOLP) in reliability test system 79 (RTS79) are estimated. The results of the simulations demonstrate that the power system becomes less reliable as the frequency of successful attacks increases.

URLhttps://ieeexplore.ieee.org/document/6939397/
DOI10.1109/PESGM.2014.6939397
Citation Key6939397