Reliability Modeling for the Advanced Electric Power Grid
Abstract
The advanced electric power grid promises a self-healing infrastructure using distributed, coordinated, power electronics control. One promising power electronics device, the Flexible AC Transmission System (FACTS), can modify power flow locally within a grid. Embedded computers within the FACTS devices, along with the links connecting them, form a communication and control network that can dynamically change the power grid to achieve higher dependability. The goal is to reroute power in the event of transmission line failure. Such a system, over a widespread area, is a cyber-physical system. The overall reliability of the grid is a function of the respective reliabilities of its two major subsystems, namely, the FACTS network and the physical components that comprise the infrastructure. This paper presents a mathematical model, based on the Markov chain imbeddable structure, for the overall reliability of the grid. The model utilizes a priori knowledge of reliability estimates for the FACTS devices and the communications links among them to predict the overall reliability of the power grid.
Recommended Citation
A. Z. Faza et al., "Reliability Modeling for the Advanced Electric Power Grid," Lecture Notes in Computer Science: Computer Safety, Reliability, and Security, vol. 4680, pp. 370 - 383, Springer Verlag, Sep 2007.
The definitive version is available at https://doi.org/10.1007/978-3-540-75101-4_35
Meeting Name
26th International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2007 (2007: Sep. 18-21, Nuremberg, Germany)
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Sponsor(s)
National Science Foundation (U.S.)
University of Missouri--Rolla. Intelligent Systems Center
Keywords and Phrases
FACTS; Reliability; Cyber-Physical; Embedded; Power Grid; Flexible AC Transmission Systems
International Standard Book Number (ISBN)
978-3540751007; 978-3540751014
International Standard Serial Number (ISSN)
0302-9743; 1611-3349
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2007 Springer Verlag, All rights reserved.
Publication Date
01 Sep 2007
Comments
Supported in part by NSF MRI award CNS-0420869, NSF CSR award CCF-0614633, and the UMR Intelligent Systems Center.