MAE Candidate Seminar – Concurrent Learning Cyber-physical Methods for Resilient Energy Delivery Systems

Date/Time

02/23/2023
12:45 pm-1:45 pm
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Location

MAE-A Room 303
939 Sweetwater Drive
Gainesville, FL 32611

Details

Concurrent Learning Cyber-physical Methods for Resilient Energy Delivery Systems

Thursday, February 23, 2023, at 12:50 pm
Location: In-Person MAE-A, Room 303

Olugbenga Moses Anubi
Assistant Professor, Electrical and Computer Engineering Department, FAMU-FSU College of Engineering

Abstract
Reliability, security, and resiliency of the energy delivery system have been an active area of research, in one form or the other, for as long as the existence of the power grid. With every advancement in grid technology comes new challenges in terms of reliability and resiliency. The past few decades have seen a sharp rise in communication and computational technologies applied to the grid. The cyber-physical layer grows exponentially as the electric power system undergoes a transformation to an extensive dependence on distributed energy resources with associated digital control and communication interfaces, many of these located beyond the grid edge.

Consequently, the problem of securing the grid has become technically more challenging on multiple fronts: (1) Ubiquity of internet of things (IoT) and industrial IoT (IIoT) devices on the power grid increases the vulnerability and threat landscape exponentially; (2) The increased variability due to the introduction of distributed energy resources (DER) makes contingency analysis challenging; (3) Despite advancements in computation technology, the sheer size of the cyber landscape often renders computational processes infeasible; and (4) The tight coupling of the cyber and physical components makes pure information technology (IT) or operational technology (OT)-based security technologies less effective.

To address these challenges, current state-of-the-art approaches often incorporate AI/ML technologies to detect anomalies in the system’s operational data. This has shown significant promise with high true positive rates when the available data adequately captures the operating conditions of the system. However, performance degradation sets in when new operation regimes not represented in the available dataset are encountered. Unfortunately, this is well known and exploited by malicious attackers to mimic extreme event situations, forcing the system into panic mode.

In this talk, I will discuss some recent results leveraging the cyber-physical nature of the energy delivery processes to develop concurrent learning resilient algorithms. These seamlessly merge data-driven machine learning models, for the cyber layer, with domain knowledge physics-based models for the physical layer to simultaneously achieve high accuracy and high generalizability for detecting, localizing, and neutralizing the effects of both known and unknown extreme events. This promises to enable energy delivery systems to survive malicious or natural extreme events while sustaining critical functions

Biography
Olugbenga Moses Anubi is an Assistant Professor in the Electrical and Computer Engineering Department at the FAMU-FSU College of Engineering, with affiliations with the Center for Advanced Power Systems (CAPS) and the Center for Intelligent Systems, Controls and Robotics (CISCOR). He is the director of the Resilient and Autonomous Systems Lab (RASLab). He received a Ph.D in Mechanical Engineering from the University of Florida. Before joining FSU, he was a Lead Control Systems Engineer at the GE Global Research Center, NY. His work within GE resulted in 15+ Patent Applications and several recognitions, including the GE Technology Award (Physical+Digital), the Connected Controls Technical Achievement Award, the Whitney Award, and the Dushman Technology Award. His work is currently supported by DOE, ONR, DARPA, and Northrop Grumman. His research interests include resilient energy delivery systems, learning systems, and control systems

Faculty Host: Katerina Aifantis

Categories

Hosted by

UF Mechanical and Aerospace Engineering