Semester
Summer
Date of Graduation
2024
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Anurag Srivastava
Committee Co-Chair
Jignesh Solanki
Committee Member
Jignesh Solanki
Committee Member
Parviz Famouri
Committee Member
Sarika Solanki
Abstract
Advanced sensing and automation are essential to managing the evolving electric power system with tight coupling of information and power system layer. However, this increasing number of cyber-physical devices brings vulnerabilities from cyber threats and extreme weather events can endanger the power system on a physical level as well. Advanced monitoring and control algorithms with human operators in the loop are needed to enable power system resiliency despite these increasing threats. These advanced algorithms require validation using a realistic test system that mimics real-world scenarios.
This work focuses on a) developing a realistic real-time cyber-power testbed with hardware-in-the-loop to generate system operating scenarios with adverse events and associated data, b) integration with supervisory control and data acquisition systems (SCADA), synchrophasor-based monitoring systems, and resiliency tools, and c) physics-aware machine learning algorithm for data anomaly detection. The developed testbed can be used to model cyber and weather events with detailed electromagnetic power system simulation using a Digital Real-Time Simulator (DRTS) and Intelligent Electronic Devices (IEDs) in the loop. The testbed is interfaced with the control room environment for validation of advanced tools and training of power system operators. Reduced-order modeling is developed using time-domain and frequency-domain simulation to meet computational limitations, followed by comprehensive validation using multiple power system simulation platforms. The testbed was used for realistic sensor data generation including anomalies for multiple operating scenarios. An algorithm has been developed using Physics-Informed Machine Learning (PIML) to identify cyber anomalies and sensor errors in synchrophasor data. It thereby helps distinguish cyber events in the network from physical events in the grid.
Recommended Citation
Sivaramakrishnan, Vasavi, "Real-Time Cyber-Power Testbed Enhancement and Synchrophasor Data Generation for Anomaly Detection using Physics-Informed Machine Learning" (2024). Graduate Theses, Dissertations, and Problem Reports. 12611.
https://researchrepository.wvu.edu/etd/12611
Embargo Reason
Publication Pending