Title of the project
Machine Learning for Dynamics Analysis of Power Electronic Systems
Background and summary of the Strategic recruitment fellowship:
Power electronics technology enables efficient electricity usage by controlling electronic devices with digital algorithms. The software-controlled, power-electronic converters have been vastly used in modern society and become a transformational technology for the energy transition. The proliferation of power-electronic converters is transforming legacy energy systems with more flexibility and improved efficiency, yet it also brings new security challenges to energy systems. In recent years, power disruptions induced by erratic interactions of converter-based energy assets are increasingly reported. Methods for the dynamics analysis of power electronic systems are urgently needed to screen instability and security risks in modern energy systems.
This project aims to leverage digital technologies to redefine the paradigm of dynamics analysis for power electronic systems. First, a trustworthy artificial intelligence (AI) modelling framework for converter-based energy assets will be established. Physical-domain knowledge will be combined with the recent advances in machine learning algorithms to make the AI model more reliable. Then, based on the AI models of power converters, a scalable and efficient dynamics analysis approach will be developed for power electronic systems, ranging from single converters to hundreds of thousands of converters. Finally, physics-based models of benchmark energy systems will be built to test the effectiveness of developed models and methods.
Research in the area of power electronics-controlled power systems. Wang is active in the broader community working in the area and will bring further visibility and provide strong leadership.
Xiongfei Wang is a Full Professor and Research Group Leader for Electronic Power Grid (eGRID) at Aalborg University and a part-time Full Professor at KTH Royal Institute of Technology, Stockholm, Sweden.