Files in this item



application/pdfWilliam_Niemira.pdf (230kB)
(no description provided)PDF


Title:Malicious data detection in state estimation leveraging system losses & estimation of perturbed parameters
Author(s):Niemira, William
Advisor(s):Sauer, Peter W.; Bobba, Rakesh
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):state estimation
parameter estimation
bad data detection
Abstract:It is critical that state estimators used in the power grid output accurate results even in the presence of erroneous measurement data. Traditional bad data detection is designed to perform well against isolated random errors. Interacting bad measurements, such as malicious data injection attacks, may be difficult to detect. In this work, we analyze the sensitivities of specific power system quantities to attacks. We compare real and reactive flow and injection measurements as potential indicators of attack. The use of parameter estimation as a means of detecting attack is also investigated. For this the state vector is augmented with known system parameters, allowing both to be estimated simultaneously. Perturbing the system topology is shown to enhance detectability through parameter estimation.
Issue Date:2013-08-22
Rights Information:Copyright 2013 William Niemira
Date Available in IDEALS:2013-08-22
Date Deposited:2013-08

This item appears in the following Collection(s)

Item Statistics