SYP Event Keynote Speakers

Dr. Subham Sahoo
Title: Semantics-Aware Cybersecurity Prognosis and Diagnosis in Power Electronics

Abstract: Increased cyber intrusions across the globe have prioritized cybersecurity enhancements for power electronics-interfaced DERs. To address this issue, many information technology-based standardized approaches typically rely on innovations in the communication layer, which does not necessarily solve the fundamental energy security problem. This is where semantics meet power, where the sampling of the communication network is unconventionally augmented with power dynamics to decipher a novel process-aware sampling criterion that responds proactively during cyber attacks on the system. This webinar will highlight new semantics-aware, non-invasive cybersecurity technologies for power electronic converters that provide a unified safeguard capability against multiple cyber-attacks. Not only does it ensure a resource-efficient design perspective, but it also makes easy customization for different DERs.

Brief Biography

He received a B.Tech. & PhD degree in Electrical and Electronics Engineering from VSSUT, Burla, India and Electrical Engineering at Indian Institute of Technology, Delhi, New Delhi, India, in 2014 & 2018, respectively. He is currently an Assistant Professor in the Department of Energy, Aalborg University (AAU), Denmark, where he is also the vice-leader of the research group on Reliability of Power Electronic Converters (ReliaPEC) in AAU Energy.
He is a recipient of the Indian National Academy of Engineering (INAE) Innovative Students Project Award for the best PhD thesis across all the institutes in India for 2019. He was also a distinguished reviewer for the IEEE Transactions on Smart Grid in 2020. He is currently the vice-chair of IEEE PELS Technical Committee (TC) 10 on Design Methodologies. He was the secretary of the IEEE YP Affinity Group, Denmark, 2020-2022. His research interests are control, optimization, cybersecurity and stability of power electronic dominated grids, physics-informed machine learning tools for power electronic systems.