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Thomas J. Overbye
Researcher at Texas A&M University
Publications - 342
Citations - 9674
Thomas J. Overbye is an academic researcher from Texas A&M University. The author has contributed to research in topics: Electric power system & Grid. The author has an hindex of 48, co-authored 308 publications receiving 8315 citations. Previous affiliations of Thomas J. Overbye include United States Department of the Army & Arkansas State University.
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Journal ArticleDOI
A power flow measure for unsolvable cases
TL;DR: In this article, the authors developed an algorithm based on the Newton-Raphson power flow algorithm to quantify the degree of unsolvability and to provide optimal recommendations of the parameters to change to return to a solvable solution.
Journal ArticleDOI
An Authenticated Control Framework for Distributed Voltage Support on the Smart Grid
TL;DR: The goal is to present how the smart grid can enable the utilization of available end-user devices as a resource to mitigate power system problems such as voltage collapse.
Proceedings ArticleDOI
SCADA Cyber Security Testbed Development
Charles M. Davis,Joseph Euzebe Tate,Hamed Okhravi,Chris Grier,Thomas J. Overbye,David M. Nicol +5 more
TL;DR: The development of a testbed designed to assess the vulnerabilities introduced by using public networks for communication is presented, to help utilities deal with cyber security threats.
Journal ArticleDOI
SCPSE: Security-Oriented Cyber-Physical State Estimation for Power Grid Critical Infrastructures
Saman Zonouz,Katherine M. Rogers,Robin Berthier,Rakesh B. Bobba,William H. Sanders,Thomas J. Overbye +5 more
TL;DR: A security-oriented cyber-physical state estimation (SCPSE) system, which, at each time instant, identifies the compromised set of hosts in the cyber network and the maliciously modified set of measurements obtained from power system sensors.
Journal ArticleDOI
An energy based security measure for assessing vulnerability to voltage collapse
TL;DR: The security measure captures nonlinear effects such as VAR limits on generators that can influence the systems vulnerability to collapse and the behavior of the index with respect to network load increases is nearly linear over a wide range of load variation, facilitating prediction of the onset of collapse.