Title
Dominoes With Communications: On Characterizing The Progress Of Cascading Failures In Smart Grid
Abstract
Cascading failures are one of the most devastating forces in power systems, which may be initially triggered by minor physical faults, then spread with Domino-like chain-effect, resulting in large-scale blackout. How to prevent cascading failures becomes imperative, as our daily lives heavily depend on stable and reliable power supply. The next-generation power system, namely Smart Grid, is envisioned to facilitate real-time and distributed control of critical power infrastructures, thus effectively forestalling cascading failures. Although cascading failures have been well investigated in the literature, most studies were confined only in the power operation domain with the assumption that communication is always perfect, which is, however, not true for today's communication networks, where traffic congestion and random delay happen. Therefore, an open question is how to characterize cascading failures in the communication-assisted smart grid? To this end, we take an in-depth inspection of cascading failures in smart grid and reveal the interactions between the power system and the communication network. Our results provide insights into the interactions between physical failure propagation and communication message dissemination. In addition, we show that while ideal communications can undoubtedly help prevent cascading failures, under-achieved communications (i.e., communications with severe delay) can, counter-intuitively, exacerbate cascading failures.
Year
DOI
Venue
2016
10.1109/ICC.2016.7511048
2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)
Field
DocType
ISSN
Telecommunications network,Smart grid,Computer science,Computer network,Electric power system,Real-time computing,Electric power transmission,Cascading failure,Blackout,Power-system protection,Traffic congestion,Distributed computing
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
5
Authors
3
Name
Order
Citations
PageRank
Mingkui Wei1174.59
Zhuo Lu212328.22
Wenye Wang31168103.99