Professor Nanpeng Yu at ECE Spring 2023 Colloquium
Machine Learning for Power Systems with Physics-Informed Methods
The electric utility industry is being swamped by petabytes of data coming from various sources such as smart meters, phasor measurement units, SCADA systems, geographical information systems, and customer management systems. The primary and secondary value imbedded in the complex and heterogeneous data sets from power systems is immense. However, algorithms and applications for unlocking the potential of big data in power systems are at an early stage of development. Although off-the-shelf machine learning algorithms could improve the efficiency, reliability and resiliency of power systems, their potential is limited by the lack of physical-domain knowledge. This talk covers how to synergistically combine machine-learning models with physical models of power system. The applications of physics-informed machine learning methods in both power distribution system and transmission systems with large-scale real world data will be presented in detail.
About Prof. Nanpeng Yu
Nanpeng Yu received his B.S. in Electrical Engineering from Tsinghua University in 2006 and Ph.D. degree from Iowa State University in 2010. He is an associate professor of Electrical and Computer Engineering at the University of California, Riverside. Yu is the recipient of the Regents Faculty Development award from University of California. He received multiple best paper and prize paper awards from the IEEE Power and Energy Society (PES) General Meetings and PES Technical Committee. Yu is the director of Energy, Economics, and Environment Research Center at UC Riverside. He is also a cooperating faculty member of department of computer science and engineering and department of Statistics. He currently serves as the chair of the distribution system operation and planning subcommittee of IEEE Power and Energy Society. Yu currently serves as the associate editor for IEEE Transactions on Smart Grid and IEEE Transactions on Sustainable Energy.