Abstract: Digital twins can enhance monitoring, prediction, control, and decision support in power-electronic systems and DC microgrids; however, their real-world deployment requires careful tradeoffs among model fidelity, computational cost, data availability, and operational objectives. This tutorial presents a practical framework for designing, integrating, and deploying hierarchical digital twins for power electronics and microgrid applications. The tutorial distinguishes digital twins from conventional simulations by emphasizing validated, bidirectional connections between physical assets, sensing data, and computational models operating in real time or faster than real time. Participants will learn how to develop component-level digital twin blocks and integrate them into system-level architectures using multi-domain, multifunction digital images that represent electrical, thermal, mechanical, and degradation behavior at appropriate fidelities and timescales. Practical examples will cover electro-thermal modeling of power converters, thermal-profile prediction for cables and critical assets, hierarchical control of DC microgrids, and forecasting-based power-flow management. Applied AI methods are discussed as an emerging enabler for forecasting, anomaly detection, and intelligent decision support within digital-twin-enabled energy systems.
Bio: Kerry Sado (Senior Member, IEEE) received his Ph.D. in Electrical Engineering from the University of South Carolina, Columbia, South Carolina, in 2024. He is currently a Research Assistant Professor in the Department of Electrical Engineering at the University of South Carolina. His research focuses on applied artificial intelligence, digital twins, power electronics, microgrids, cyber-physical energy systems, and decision-making frameworks for defense-related power and energy applications.
Dr. Sado has experience developing modeling, simulation, and hardware-in-the-loop solutions for power-electronic converters and complex energy systems. His work includes hierarchical digital twin architectures for DC microgrids and naval power systems, real-time electro-thermal modeling of power converters, thermal prediction of critical power-system assets, and forecasting-based power-flow management. He is particularly interested in combining physics-based models, real-time data, and applied AI to improve system monitoring, prediction, control, resilience, and operational decision support.
Register here: https://ieee.webex.com/weblink/register/r654b018cc26bbe986ad98ccfbe7912b6