TUT1: Recent Advances, Challenges and Future Trends of Machine Learning Applied to Power Electronics and Signal Processing
Prof. Felipe Ruiz Allende Universidad San Sebastián, Chile
Tue 04-Mar-2026, TBA, Room TBA
Abstract:
In recent years, the scientific and industrial communities have witnessed a remarkable expansion of Machine Learning (ML) across a wide range of engineering disciplines. This rapid development has been strongly enabled by the increasing computational capabilities of modern embedded platforms, particularly Field-Programmable Gate Arrays (FPGAs) and heterogeneous System-on-Chip (SoC) architectures, which excel at high-speed parallel computations and real-time processing. As a result, AI-based solutions have become increasingly attractive for time-critical applications. This growing interest is also evident in the fields of power electronics, where traditional model-based approaches often face limitations related to parameter uncertainty, nonlinear dynamics, unmodeled effects, and changing operating conditions. In this context, data-driven techniques have emerged as a powerful alternative or complement to classical methods, particularly in predictive control, where robustness and computational efficiency are critical requirements.
This tutorial will address the latest advances, challenges, and emerging trends in machine learning applied to power converters and signal processing, with a strong emphasis on practical implementation. Participants will gain insight into the fundamental principles of machine learning, explore state-of-the-art applications, and understand the trade-offs between accuracy, robustness, explainability, and real-time feasibility. The tutorial is designed to bridge the gap between academic research and industrial deployment, providing both theoretical foundations and application-oriented perspectives. Through illustrative case studies and implementation insights, attendees will learn how machine learning is reshaping modern power electronic systems and signal processing techniques.
Topic: Introduction and Motivation (Prof. Giovanny Sanchez)
- Motivation Machine Learning in Power Electronics and Signal processing
- Limitations of classical model-based control and signal processing techniques
- Overview of Machine Learning paradigms: supervised, unsupervised, and reinforcement learning
- Role of modern embedded platforms (FPGA, SoC) in enabling AI-based real-time solutions
Topic: Machine Learning Techniques Applied to Power Electronics (Prof. Felipe Ruiz Allende)
- Data-driven of power converters
- AI-based Predictive Control:
- Cost function formulation
- Weighting factor optimization using ANN
- Robustness under parameter mismatch and unmodeled dynamics
- Comparative analysis: model-based vs. data-driven predictive control
Topic: Implementation Challenges and Real-Time Considerations (Prof. Felipe Ruiz Allende)
- Real-time constraints in AI-based control and signal processing
- FPGA and embedded implementation aspects
- Computational burden, latency, and memory considerations
- Explainability, generalization, and safety issues
Topic: Adaptive Signal Processing and Filtering for AI-Driven Applications (Prof. Juan Gerardo Avalos)
- Signal preprocessing for power electronics and sensor data
- Adaptive filtering, spectral coherence, and noise mitigation
- Real-time signal conditioning using ML-based estimators
- Case examples: adaptive signal reconstruction and quality assessment
Topic: Future Trends and Industrial Perspectives (Prof. Giovanny Sanchez)
- Emerging directions: hybrid physics-informed ML/MPC
- Explainability, safety, and certification challenges
- AI for smart grid integration, predictive maintenance, and advanced converters
- Collaborative opportunities between industry and academia
Speaker Bio:
Prof. Felipe Ruiz Allende (Member, IEEE) received the M.Sc. degree at Universitat Politecnica de Catalunya, Spain, 2010. PhD double degrees in electronics engineering between Universidad Tecnica Federico Santa Maria, Valparaiso, Chile, and Warsaw University of Technology, Poland, 2023. Currently, he joined the Energy Transition Center, USS, where he is currently a researcher. His current research interests include power electronic modular multilevel converters, solid-state transformers, and the application of Machine Learning, Artificial Intelligence, and data-driven techniques to power electronics, predictive control, and signal processing, with particular emphasis on model-free and adaptive control strategies for grid-connected power converters.
TUT2: Resilient Energy Management Systems of Microgrids against Extreme Events
Prof. Mo-Yuen Chow Shanghai Jiao Tong University, China
Prof. Shichao Liu Carleton University, Canada
Wed 05-Feb-2026, TBA, Room TBA
Abstract:
Description: The aging of the power infrastructure and the increasing frequency of extreme events result in more catastrophic power outages. One promising solution to enhance the resiliency of power systems is to deploy microgrids. A microgrid can operate in either a grid-connected mode during normal operating conditions or an islanded mode (isolated mode) in the event of faults or outages in the main grid. During and after natural disasters, microgrids act in islanded mode to support critical demands, such as hospital communities and campuses, that are equipped with local DGs and DESs. While most of the existing work have focused on energy management of microgrids under normal operations, resilient control and operation are imperative to enhance microgrids’ stability and relibility. This tutorial discusses most recent progresses in resilient control and energy management of microgrids under extreme events such as natural disasters and cyber attacks, to yield relevant insights and promote exchange of experiences on resilience-oriented approaches for better energy management of microgrids in IES communities.
Topics:
- Resilient Distributed Control for Networked Microgrids under Natural Disasters
- Adaptive Cost Function Approach for Networked Microgrids under Natural Disasters
- Resilient Distributed Secondary Frequency Regulation and Power Sharing in Microgrids under Cyberattacks
- Stochastics Bayesian Game for Securing Secondary Frequency Regulation of Microgrids against Cyberattacks with Incomplete Information
- A Neural Fictious Self-Play Anti-Jamming Game for Secondary Frequency Regulation in Microgrids against Cyberattacks with Imperfect Observations
Speaker Bio:
Prof. Mo-Yuen Chow (Fellow, IEEE) earned his degree in Electrical and Computer Engineering from the University of Wisconsin-Madison (B.S., 1982); and Cornell University (M. Eng., 1983; Ph.D., 1987). Dr. Chow joined as a Professor at UM-Shanghai Jiao Tong University Joint Institute in 2022. He was a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. Dr. Chow’s recent research focuses on distributed control and management, smart micro-grids, batteries management, and mechatronics systems. Dr. Chow has established the Advanced Diagnosis, Automation, and Control Laboratory. He is an IEEE Fellow, the Co-Editor-in-Chief of IEEE Trans. on Industrial Informatics 2014-2018, Editor-in-Chief of IEEE Transactions on Industrial Electronics 2010-2012. He has received the IEEE Region-3 Joseph M. Biedenbach Outstanding Engineering Educator Award, the IEEE ENCS Outstanding Engineering Educator Award, the IEEE ENCS Service Award, the IEEE Industrial Electronics Society Anthony J Hornfeck Service Award, and the IEEE Industrial Electronics Society Dr.-Ing. Eugene Mittelmann Achievement Award. He is a Distinguished Lecturer of IEEE Industrial Electronics Society.
Prof. Shichao Liu (Senior Member, IEEE) received the B.Sc. and M.Sc. degrees in control engineering from Harbin Engineering University, Harbin, China, in 2007 and 2010, respectively, and the Ph.D. degree in electrical and computer engineering from Carleton University, Ottawa, ON, Canada, in 2014. He is currently an Associate Professor with the Department of Electronics, Carleton University. His research interests include AI empowered analytics, operation and control of cyber-physical energy systems with applications in microgrids and large-scale power systems. He received the John R. Evans Leaders Fund from Canada Foundation of Innovation and the Research Achievement Award from Faculty of Engineering and Design from Carleton University. He received Best Paper Award in IEEE ICMA 2025 and Best Paper Runner-Up Award from IEEE EPEC 2022. He is Vice Chair of IEEE IES TC on Resilience and Security in Industrial Application. He is an Associate Editor for IEEE Transactions on Network Science and Engineering, IEEE Transactions on Industrial Cyber-Physical Systems, IEEE Access, International Journal of Robotics and Automation, Frontiers in Control Engineering, and a Member of the Editorial Board of Smart Cities.
TUT3: Quantum-Resilient Security for Industrial 6G and Cyber-Physical Systems
Prof. Abdullah Aydeger Florida Institute of Technology, USA
Dr. Engin Zeydan Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain
Fri 06-Feb-2026, TBA, Room TBA
Abstract:
Industrial cyber physical systems increasingly rely on advanced wireless connectivity, edge computing, and distributed control to support automation, robotics, and critical infrastructure. Emerging 6G networks are expected to play a central role in enabling these systems by providing ultra reliable and low latency communication across industrial environments. At the same time, advances in quantum computing pose a long-term threat to classical cryptographic mechanisms that currently protect industrial communication and control channels. Since industrial systems often have long operational lifetimes, addressing quantum security risks early is critical.
This tutorial focuses on quantum resilient security mechanisms for industrial cyber physical systems, with emphasis on post quantum cryptography and quantum based key management. The tutorial introduces the fundamental security challenges posed by quantum adversaries and discusses how quantum safe mechanisms can be integrated into industrial 6G and next generation industrial communication architectures. A key focus is placed on practical deployment considerations, including computational overhead, memory usage, latency impact, and system reliability in resource constrained industrial devices. The tutorial combines conceptual foundations with practical demonstrations and case studies using current user equipment platforms as realistic precursors to future industrial 6G devices. The goal is to provide attendees with actionable insight into designing secure and future proof industrial cyber physical systems in the post quantum era.
Topics:
- Quantum security threats and their impact on industrial cyber physical systems
- Fundamentals of post quantum cryptography for industrial and embedded platforms
- Quantum key distribution concepts and applicability to industrial environments
- Integration of quantum resilient security into industrial 6G and cyber physical architectures
- Practical demonstrations and performance evaluation on resource constrained devices
- Open challenges and research directions for quantum safe industrial systems
Speaker Bio:
Prof. Abdullah Aydeger is currently an assistant professor at the Electrical Engineering and Computer Science Department at FIT. Prior to joining FIT in August 2022, he was an assistant professor at the School of Computing at Southern Illinois University, Carbondale, since 2020. Dr. Aydeger obtained a Ph.D. Degree in Computer and Electrical Engineering from Florida International University in 2020. His research interests are post-quantum cryptography, network security, and virtualization.
Dr. Engin Zeydan received a PhD degree in February 2011 from the Department of Electrical and Computer Engineering at Stevens Institute of Technology, Hoboken, NJ, USA. Since November 2018, he has been with the Services as Networks (SaS) Research Unit of the CTTC working as a Senior Researcher. He was a part-time instructor at Electrical and Electronics Engineering department of Ozyegin University Istanbul, Turkey between January 2015 and June 2018. His research areas include data engineering/science for telecommunication networks and network security.