Reliability Modeling and Analysis of Cyber Enabled Electric Power Systems

Abstract

Cyber-induced failures affect power system reliability and thus are important to be considered in composite system reliability evaluation. This dissertation extends the scope of bulk power system reliability modeling and analysis with the consideration of cyber elements. A novel methodology by introducing the concept of Cyber-Physical Interface Matrix (CPIM) is proposed. The failure modes of cyber components and their impact on transmission line tripping behaviors are modeled and numerically analyzed as an example to illustrate the construction and utility of the CPIM. The methodology is then enhanced and implemented on an extended Roy Billinton Test System (RBTS) with its applicability for large systems illustrated. The results clearly show the impact of cyber-induced failures on system-wide reliability indices. The CPIM is the critical idea in the proposed methodology. It decouples the analysis of the cyber part from the physical part and provides the means of performing the overall analysis in a tractable fashion. The overall methodology proposed in this dissertation also provides a scalable option for reliability evaluation of large cyber-physical power systems. The efficiency of the overall methodology can be further improved with the use of non-sequential Monte Carlo techniques. However, the failure and repair processes in cyber-induced events are inherently sequential involving dependent failures, making it difficult to utilize non-sequential sampling methods as simply as when the components are independent. In this dissertation, the difficulties of using sampling when there are dependent failures are thoroughly explored. An approach is proposed to overcome the difficulties by generating a representative state space and its probabilities from which states can be sampled. The proposed approach not only preserves the sequential and dependent features of cyber-induced events but also improves the efficiency, which is very beneficial for reliability evaluation of large power systems in the presence of cyber-induced dependent failures

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