This dissertation addresses the growing significance of cybersecurity in electric drive systems with applications on power systems, electric vehicles, and intelligent manufacturing systems as these domains increasingly rely on digital technologies and interconnectivity. This dissertation presents a comprehensive approach to tackle cybersecurity challenges in three key areas: (1) cyber-physical modeling of intelligent electric drive systems, (2) development of a cyber-physical testbed for modern industrial motor drives, and (3) implementation of advanced anomaly detection and root-cause diagnosis algorithms. The proposed methodology for modeling intelligent electric drive systems integrates physics-equation-based and control information flow models, creating an analytical framework that encompasses both physical faults and cyberattacks. This framework bridges the gaps between current modeling methods employed by different communities and facilitates seamless connections between existing modeling approaches from the cyber and physical domains. The development of a cyber-physical testbed for modern industrial motor drives includes a hardware-in-the-loop (HIL) real-time simulation testbed and a lab-scale real-world hardware experiment testbed. The HIL testbed mitigates potential risks and costs during the research and development process, while the lab-scale hardware experiment testbed focuses on real-world data generation, system final validation, and prototype demonstration. Furthermore, this dissertation develops data-driven anomaly detection and root-cause diagnosis methods to address critical problems in existing monitoring systems for both cyberattacks and physical faults. The proposed methods reduce time-to-detect, achieve high diagnostic accuracy, enable monitoring of multiple motor drives with limited sensors, and decrease dependence on large amounts of high-cost and high-risk experimental data sets. In the end, a protype is built for intelligent electric drive systems to facilitate real-world security demonstrations, which integrates attack modeling and analysis, detection, and root cause diagnosis. In summary, this dissertation provides a promising direction for future research and development efforts aimed at enhancing the cybersecurity of electric drive systems in critical power systems, electric vehicles, and intelligent manufacturing systems.
