【摘要】 In this talk, we will discuss a couple of analysis and operation methods for complex cyber-physical systems, mainly power and energy systems. We first illustrate the change point detection techniques for the SCADA data-based prognostics and health management of wind turbines. We also demonstrate various ML techniques for time series forecasting in power and energy systems. Finally, we discuss the dynamic mitigation of power grids subject to extreme events, such as bushfires and cyber-attacks, in an online manner.

【个人介绍】Dr. Huadong Mo is now a Senior Lecturer at the School of Systems and Computing, University of New South Wales, Australia. Before joining UNSW in 2019 as a Lecturer, he was a postdoc at the Department of Mechanical and Process Engineering, EHTZ, Switzerland, from 2016 to 2019. He received his B.E. degree in automation from the University of Science and Technology of China, China in 2012 and his Ph.D. degree in systems engineering and engineering management from the City University of Hong Kong, Hong Kong, in 2016. His research interests include advanced sensing and data analysis for prognostics and health management, sustainable transportation and energy systems, robust operation of power and energy systems under extreme events, and (Quantum-inspired) reinforcement learning-based lifelong asset management.