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A stochastic Petri nets approach to dependability analysis of control center networks in smart grid

Authors: Rongfei Zeng; Xuemin Shen; Yixin Jiang; Chuang Lin;

A stochastic Petri nets approach to dependability analysis of control center networks in smart grid

Abstract

As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average