Date of Graduation
Doctor of Philosophy in Engineering (PhD)
Second Committee Member
Third Committee Member
Cybersecurity, Low Latency Detection, Power System, Smart Grid
The transformation of traditional power grids into smart grids has seen more new technologies such as communication networks and smart meters (sensors) being integrated into the physical infrastructure of the power grids. However, these technologies pose new vulnerabilities to the cybersecurity of power grids as malicious attacks can be launched by adversaries to attack the smart meters and modify the measurement data collected by these meters. If not timely detected and removed, these attacks may lead to inaccurate system state estimation, which is critical to the system operators for control decisions such as economic dispatch and other related functions.
This dissertation studies the challenges associated with cyberattacks in power grids and develops solutions to effectively and timely detect these attacks to ensure an accurate state estimation. One of the common approaches to improving the state estimation accuracy is to incorporate phasor measurement unit (PMU) devices into the system to provide extra and more secure measurements. In this research, we design algorithms that place PMUs at strategic locations to enhance the system's state estimation accuracy and its capability to detect cyberattacks. This approach of installing PMU devices in power grids, nonetheless, does not guarantee a timely attack detection that is critical for a timely deployment of countermeasures to prevent catastrophic impacts from the attacks. Thus, the low latency intrusion detection problem is studied to reduce attack detection delays. The state estimation and intrusion detection problem is further extended to a dynamic power system, where there are sudden changes in system loads.
Akingeneye, I. Z. (2018). Low Latency Intrusion Detection in Smart Grids. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2740