Plenary Speech 9

Plenary  Speech 9 :Generalized Optimal Pulse Pattern and Predictive Control 

for High Power Converter Clusters

Zhenbin Zhang (IET Fellow) received the Ph.D. degree at the Institute for Electrical Drive Systems and Power Electronics  (EAL), Technical University of Munich (TUM), Germany, with “summa cum laude”. From 2016 to 2017, he worked as a Research Fellow and the group-leader for “Modern Control Strategies for Electrical Drives” group in EAL. Since 2017, he has held the position of full professor and International Collaboration Ambassador of Shandong University, China. From 2018 to 2022, he was a guest professor in TUM with the “August-Wilhelm Scheer Professorship Award." Prof. Zhang focuses on design and (predictive) control of renewable energy and power conversion systems, including interests of power electronics and motor drives. micro-grids with energy storage, and predictive maintenance of power conversion systems. Prof. Zhang is an IET Fellow, IET Chartered Engineer, IEEE Senior Member. and World Top 2% Scientists released by Stanford University. He was awarded the “VDE-AWARD-2017” for his contributions in advanced control for wind power generation and electrical drives. He was selected for the “1000-Talent” national program of China, and granted the Science Fund for Distinguished Young Scholars of Shandong Province, China. In addition, he won the First Prize of Science and Technology Award of China Electrotechnical Society, the Second Prize of both Shandong and Anhui Provincial Science and Technology Progress Award in 2023, 2022 and 2021, respectively, for his contributions in design and control of renewable energy and power conversion systems.

Abstract: Model Predictive Control (MPC) has gained significant attention for its high-performance control and its ability to handle multiple objectives and constraints in power electronics. As energy conversion systems scale up in capacity and complexity—particularly in clustered configurations—traditional modulation and control strategies face increasing limitations in terms of efficiency, scalability, and adaptability. This keynote introduces an advanced predictive control framework for high-power energy conversion clusters, where multiple converters operate under strict performance and efficiency requirements. By integrating generalized pulse pattern optimization theory, the method improves power quality, reduces losses, and enables precise cluster control. It addresses challenges like inter-converter coupling and switching nonlinearity through a unified theoretical approach, bridging predictive control with pulse-level modulation. The talk will explore potential applications, implementation strategies, and future research directions, discussing their impact on the future of next-generation power electronics.