Hybrid Model-Based and Data-Driven Small-Signal Stability and Oscillation Analysis ... | 13 Mar 25 скачать в хорошем качестве

Hybrid Model-Based and Data-Driven Small-Signal Stability and Oscillation Analysis ... | 13 Mar 25

Presentation Title Hybrid Model-Based and Data-Driven Small-Signal Stability and Oscillation Analysis for Modern Power Systems with Complex Operational Dynamics Presentation Abstract: Modern power systems are required to meet increasingly stringent demands for resilience, reliability, and stability while operating under highly complex dynamic conditions. These challenges are driven largely by the growing penetration of inverter-based resources (IBRs) and their tight interactions with synchronous generators, network dynamics, and diverse load behaviors. Under such conditions, conventional small-signal stability analysis becomes inadequate due to the strong coupling among subsystems and the rapidly evolving operating states of modern power grids. In particular, the dynamics associated with power networks and control systems are deeply intertwined, resulting in complex small-signal behavior that complicates both stability assessment and oscillation analysis. Further, identifying and characterizing oscillatory modes with distinct frequency features becomes especially challenging, posing practical difficulties for real-world system operation and control. To address these challenges, this presentation introduces a hybrid and modular framework that integrates model-based and data-driven approaches for small-signal stability and oscillation analysis. The proposed framework systematically captures a broad spectrum of system dynamics across synchronous generators, IBRs with different control paradigms (e.g., Grid-Forming [GFM] and Grid-Following [GFL] control), network components, and loads. Each subsystem is modeled as a modular unit, enabling scalable system assembly and efficient small-signal and oscillation analysis. Furthermore, for “black-box” “vendor-specific” models, particularly for IBRs, data-driven surrogate models are developed to simultaneously capture large-signal trajectories and small-signal characteristics. Representative case studies are presented to demonstrate the effectiveness and practicality of the proposed framework for modern power systems with complex operational dynamics. Short Bio of the Presenter Xiaonan Lu received his B.E. and Ph.D. degrees in electrical engineering from Tsinghua University, Beijing, China, in 2008 and 2013, respectively. From September 2010 to August 2011, he was a guest Ph.D. student at the Department of Energy Technology, Aalborg University, Denmark. From October 2013 to December 2014, he was a Postdoc Research Associate at the University of Tennessee, Knoxville. From January 2015 to July 2018, he was with Argonne National Laboratory, first as a Postdoc Appointee and then as an Energy Systems Scientist. From July 2018 to July 2022, he was at Temple University as an Assistant Professor. In August 2022, he joined Purdue University as an Associate Professor, and in July 2024, he joined Argonne National Laboratory as a Research Scientist through a Purdue–Argonne Joint Appointment. Dr. Lu’s research interests include modeling, control, and design of power electronic inverters, hybrid AC/DC microgrids, and large-scale power electronics intensive power systems. He currently serves as Co-Editor-in-Chief of IEEE Transactions on Power Electronics and as Associate Editor for IEEE Transactions on Industrial Electronics, IEEE Transactions on Industry Applications, and IEEE Journal of Emerging and Selected Topics in Power Electronics. He is also the Lead Technical Program Chair for the IEEE Energy Conversion Congress and Expo (ECCE) 2025. Dr. Lu is the recipient of the 2020 IEEE Philadelphia Section Young Engineer of the Year Award, and the 2024 IEEE Power and Energy Society Central Indiana Section Outstanding Engineer of the Year Award. He also receives the 2024 Purdue University Departmental Outstanding Faculty Award in Discovery (Research), and the 2025 Purdue University Acorn Award for Research.