scispace - formally typeset
T

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.

Papers
More filters
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

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

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.