Masters Theses

Keywords and Phrases

Recovery Strategy; Survivability Index


"Critical infrastructure systems are increasingly reliant on cyber infrastructure that enables intelligent real-time control of physical components. This cyber infrastructure utilizes environmental and operational data to provide decision support intended to increase the efficacy and reliability of the system and facilitate mitigation of failure. Realistic imperfections, such as corrupt sensor data, software errors, or failed communication links can cause failure in a functional physical infrastructure, defying the purpose of intelligent control. As such, justifiable reliance on cyber-physical critical infrastructure is contingent on rigorous investigation of the effect of intelligent control, including modeling and simulation of failure propagation within the cyber-physical infrastructure.

To this end, this thesis investigates the reliability and survivability of a cyber-physical power grid based on the IEEE 9-bus test system. The research contributions include quantitative modeling of both non-functional attributes, based on data from N-1 contingency analysis that considers failures in physical and cyber components of the system. The resulting survivability model is utilized in determining the "importance" of each transmission line. The final research contribution is identification of optimal recovery strategies for the system, where the objective is to maintain the highest possible survivability in the course of recovery."--Abstract, page iii.


Sedigh, Sahra

Committee Member(s)

Hurson, A. R.
Choi, Minsu


Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering


Name appears incorrectly on title page: Albasarwi


Missouri University of Science and Technology

Publication Date

Spring 2014


viii, 46 pages

Note about bibliography

Includes bibliographical references (pages 42-45).


© 2014 Murtadha Nabeel Albasrawi, All rights reserved.

Document Type

Thesis - Open Access

File Type




Subject Headings

Smart power grids -- Mathematical models
Intelligent control systems

Thesis Number

T 10432

Electronic OCLC #