Data-driven Impedance Profile Prediction for Grid-Connected Converters

Date: 03/04/2025

Time: 10:00 am

Presenter: Yang Wu

Abstract: Data-driven approach is promising for predicting impedance profile of grid-connected voltage source converters (VSCs) under a wide range of operating points (OPs). However, the conventional approaches rely on a one-to-one mapping between operating points and impedance profiles, which can be invalid for multi-converter systems. To tackle this challenge, this webinar will introduce a stacked-autoencoder-based machine learning framework for the impedance profile predication of grid-connected VSCs, together with its detailed design guidelines. The proposed method uses features, instead of OPs, to characterize impedance profiles, and hence, it is scalable for multi-converter systems. Another benefit of the proposed method is the capability of predicting VSC impedance profiles at unstable OPs of the grid-VSC system. Such prediction can be realized solely based on data collected during stable operation, showcasing its potential for rapid online state estimation. Experiment results on both single-VSC and multi-VSC systems will be presented to validate the effectiveness of the proposed method. Applied in industry, the proposed method can predict comprehensive set of impedance profiles for power converters to ensure system stable operation.

Yang Wu received the B.Eng. and Ph.D. degrees in electrical engineering from the Department of Electrical Engineering, Tsinghua University, Beijing, China in 2017 and 2022, respectively. She is currently a Marie Skłodowska-Curie Postdoctoral Researcher with the Department of Energy, Aalborg University, Denmark. She also serves as the Technical Specialist for the Denmark startup company AI STABILITY. She was a guest Postdoctoral Researcher with the Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden, in 2024. Her research interest includes modeling and stability analysis of power converters, condition monitoring and health management of electrical assets, and advanced sensing techniques. She has authored or co-authored more than 35 research articles in peer-reviewed journals and conferences. She also holds more than 15 patents in the U.S. and China.

Dr. Wu has received the EU Marie Skłodowska-Curie Postdoctoral Fellowship, IEEE IAS PhD Thesis Award, EECS Rising Star by Georgia Tech, U.S., Outstanding PhD Graduate Award from Beijing Ministry of Education, and Excellent Graduate Award from Tsinghua University. She was also the recipient of two Best Paper and Presentation Awards at international conferences. She serves as the member of IEEE PELS Technical Committee TC10, IEEE PELS WiE & DEI Committee, and Program Committee of several international conferences (including IEEE ITSC 2022, IEEE ECCE 2024, and PEDG 2025).

Data-driven Impedance Profile Prediction for Grid-Connected Converters

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