IES Webinar Series
[TC Webinar] Low-Carrier-Ratio Modulation and Control Strategies for Dual Three-Phase Permanent-Magnet Synchronous Motor Drives
Wednesday 16 July 2025 at 8:00 PM CST, 2:00 PM CET, 8:00 AM EDT By Zheng Wang (School of Electrical Engineering, Southeast University, China) Register now using the link below: https://attendee.gotowebinar.com/register/663814100366350425 Abstract: With increasing requirements of high power ratings and high reliability in electrified transportation and energy harvesting applications, multiple three-phase permanent-magnet drives, such as the dual three-phase permanent-magnet synchronous motor (DTP-PMSM) drives have attracted more attentions in both academia and industry. For high-power or high-operation-frequency motor drives, the carrier ratio, i.e., the ratio of switching frequency versus fundamental frequency, is low. Such low-carrier-ratio operation brings challenges in large low-order harmonics, distinct control delay and coupling effects between d-axis and q-axis to control of DTP-PMSM, which suffers from much smaller inductances in harmonic subspace and more numbers of switching states. For addressing the issues and improve the control performance of DTP-PMSM with low carrier ratios, this webinar will investigate multiple advanced low-carrier-ratio modulation and control schemes for DTP-PMSM drives. The modulation schemes under investigation include multisampling space vector modulation (MS-SVM), selective harmonic elimination pulse width modulation (SHEPWM), and synchronous optimal pulse-width modulation (SOPWM), whereas the control schemes include complex vector based control, model predictive control (MPC), and flux trajectory control-based ...
[WiE Webinar] From Physics to Machine Learning and Back: Applications in Dynamical Systems
Tuesday 15 July 2025 at 3:00 PM CET, 9:00 AM EDT By Olga Fink (EPFL - École Polytechnique Fédérale de Lausanne, Switzerland) Joint Webinar with IEEE Women in IES Register now using the link below: https://attendee.gotowebinar.com/register/5997277825212029023 Abstract: This talk explores how integrating physical laws and domain knowledge can address deep learning’s limitations in modeling dynamical systems. It focuses on embedding conservation laws and structural biases—such as in physics-informed graph neural networks—and highlights symbolic regression methods for discovering interpretable, reliable models rooted in physical principles. Presenter’s bio: Olga Fink has been assistant professor of Intelligent Maintenance and Operations Systems at EPFL since March 2022. Olga’s research focuses on Hybrid Algorithms Fusing Physics-Based Models and Deep Learning Algorithms, Hybrid Operational Digital Twins, Transfer Learning, Self-Supervised Learning, Deep Reinforcement Learning and Multi-Agent Systems for Intelligent Maintenance and Operations of Infrastructure and Complex Assets. Before joining EPFL faculty, Olga was assistant professor of intelligent maintenance systems at ETH Zurich from 2018 to 2022, being awarded the prestigious professorship grant of the Swiss National Science Foundation (SNSF). Between 2014 and 2018 she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW). Olga received her Ph.D. degree from ETH ...