Grid Resilience Against Wildfire with Machine Learning: Machine Learning based Detection, Localization and Mitigation of the Impact of Forest Fires on Power Grids
Abstract
Decision-making by human operators, using system data obtained from bulk transmission systems, under adverse dynamic events should be supplemented by intelligent proactive control based on state-of-the-art machine learning (ML) algorithms. This chapter focuses on the integration of ML into transmission system operation during wildfires for resiliency-driven proactive control for load shedding, line switching, and resource allocation, considering the dynamics of the wildfire and failure propagation through the power grid to minimize impact on the system.
Type
Venue
Big Data Application in Power Systems, 393–417. Elsevier, 2024
Authors
Ph.D. 2023
Salah Uddin joined the department of Computer Science at the University of Houston as a Ph.D. student in fall 2018
and started working in the Resilient Networks and Systems Lab in spring 2020. He defended his Ph.D. dissertation in
2023. Earlier, he graduated with a Bachelor’s Degree in Computer Science and Engineering from Daffodil International
University, Bangladesh. He graduated first in his class (highest CGPA in class). He worked as a Software engineer at
Eyeball Networks Inc. and IPvision Canada Inc. from 2014 to 2018. He mainly worked with VoIP and messaging protocols
like STUN, TURN, XMPP servers, and client-side SDKs. His research interest includes cybersecurity and artificial
intelligence. Currently, his research work focuses on IoT Remote Attestation, which includes exploring IoT
vulnerabilities and attack detection.
Associate Professor
Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.
His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.
His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.