Leveraging AI and IoT for Enhanced Reliability in Smart Electrical Power Systems
Norasage Pattanadech
King Mongkut's Institute of Technology, Thailand
Dr. Norasage Pattanadech received his Bachelor's and Master's degrees (in Electrical Engineering) from King Mongkut's Institute of Technology, Ladkrabang (KMITL) and Chulalongkorn University, Bangkok, Thailand, in 1998 and 2002, respectively. He also received Dr.techn (in Engineering Sciences Electrical Engineering) from the Institute of High Voltage Engineering and System Management, Graz University of Technology, Austria 2013. He is now working in the Electrical Engineering Department, School of Engineering, KMITL. He has more than 20 years of experience in the High Voltage Testing and Analysis field, especially in condition monitoring of high voltage equipment such as rotating machine insulation assessment, underground cable remaining condition analysis and remaining lifetime assessment, transformer remaining lifetime assessment, and root cause analysis of underground cable failures. He has served on the IEC TC42 MT 23 committee: Maintenance of IEC 60270: High-Voltage Test Techniques: Partial Discharge Measurement and MT 14 committee: Maintenance of IEC 62478: High-Voltage Test Techniques: Measurement of Partial Discharge by Electromagnetic and Acoustic Method. He received a Japanese patent in 2021 for a breaker with electrode-controlled current. He is the author/co-author of more than 100 publications and four books on Electrical Engineering material and PD measurement. He is an IEEE PES Thailand member. He served as the 8th International Conference on Condition Monitoring and Diagnosis (CMD 2020), Chairperson and also the 14th International Conference On The Properties and Applications of Dielectric Materials (ICPADM 2024), Chairperson. He also serves as an Associate Editor in the section Electrical Insulating Material for the IET: Electrical Materials and Applications journal since 2024.
Abstract:
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is gradually transforming the electric power system by allowing smarter, more resilient, and adaptive grid operations. This presentation addresses a comprehensive trend of AI techniques, their application domains, and limitations in electrical power systems, followed by an exploration of IoT integration in electrical power systems. Non-invasive smart sensors combined with IoT and edge processing provide real-time monitoring, predictive maintenance, and fault localization. Machine learning applications in power system resilience are discussed in four key areas: outage prediction, forecasting, stability assessment and control, and system restoration, where ensemble learning, deep learning, and reinforcement learning demonstrate significant advantages. Furthermore, the utilization of Digital Twin technology is discussed as a dynamic tool for monitoring, simulation, planning, and resilience enhancement. Finally, this talk discusses the AI-based approaches for partial discharge detection, localization, and classification, emphasizing the role of data-driven models in improving system reliability and safety. Partial discharge monitoring and analysis are critically important for ensuring the reliability of high-voltage equipment. This significance extends beyond modern electrical power systems to encompass crucial equipment in emerging fields such as advanced transportation and scientific research, where PD analysis is vital for effective maintenance. In combination, these developments highlight the critical role of AI, IoT, and Digital Twins in building intelligent, adaptive, and secure electrical power systems.
From Geo-AI to Intelligence of Things: Building Cognitive Digital Twins for Sustainable Smart Environments
Abolghasem Sadeghi-Niaraki
XR Research Center, Sejong University, Seoul, Republic of Korea
Abolghasem Sadeghi-Niaraki (Member, IEEE) is an internationally recognized expert in Geo-AI, XR technologies, and spatial data science. He currently serves as an Associate Professor in the Department of Computer Science and Engineering and as a core member of the eXtended Reality (XR) Metaverse Research Center at Sejong University, Republic of Korea. Previously, he held the position of Assistant Professor at the Geo-Informatics Engineering Department at INHA University, South Korea. He is also a Fellow at the Spatial Data Lab (SDL), affiliated with the Center for Geographic Analysis at Harvard University. He earned his B.Sc. and M.Sc. degrees in Geomatics and GIS Engineering from K. N. Toosi University of Technology (KNTU) and completed his Ph.D. in Geo-Informatics Engineering at INHA University. He has extensive teaching and research experience at several universities, including Sejong University, Inha University, and KNTU. With over 200 peer-reviewed publications, 22+ patents, and recognition as a Top 2% Scientist worldwide (Stanford–Elsevier, 2024), his research encompasses Geospatial Artificial Intelligence (Geo-AI), XR (VR/AR/MR), Metaverse, Ubiquitous GIS, IoT, Human-Computer Interaction (HCI), and Culture Technology (CT). He has also played a strategic role in the creation of multiple international centers and dual-degree programs in collaboration with KAIST and ETRI. His interdisciplinary approach, combining technological innovation with human-centered design, has earned him prestigious honors, including the Australian Endeavour Research Fellowship and multiple national and ministerial awards.
Abstract:
The next generation of smart cities requires a deep fusion of geospatial intelligence, AI, and IoT to evolve into self-learning, context-aware environments. This keynote introduces the concept of Cognitive Digital Twins—dynamic, AI-powered representations of physical spaces that integrate Geo-AI, sensor networks, and extended reality (XR) technologies. Drawing from real-world research in spatial analytics, environmental monitoring, and XR-based visualization, the talk explores how these intelligent twins can predict urban risks, optimize mobility, and manage energy systems in real-time. By leveraging AIoT (AI + IoT) and spatiotemporal big data, this paradigm bridges the digital-physical divide and establishes a foundation for sustainable smart city ecosystems. Applications in flood mapping, air quality prediction, and metaverse-based urban planning will be highlighted.