Data Science and Machine Learning for Modern Power Systems
Course Director: Dr. Le Xie
Course Dates: December 1 – 3, 2020
Course Time: 12 pm – 4 pm CST
Hours: CEU 1.2, PDH 12
The course is designed to provide state-of-the-art introduction of data science and machine learning that is tailored for power engineering applications. The electricity industry is transforming itself from a hierarchical, passive, and sparsely-sensed engineering system into a flat, active, and ubiquitously-sensed cyber-physical system. The emerging multi-scale data from synchrophasors, smart meters, weather, and electricity markets offers tremendous opportunities as well as challenges for the industry to dynamically learn and adaptively control a smart grid.
Expected Deliverables/Learning Outcome
Participants will have access to all the course materials, including lecture notes, computer simulation codes, and individual discussions with the instructors. This training introduces the foundation of high dimensional spaces and data analytical tools necessary to model and operate a modern power system. We will introduce a suite of tools for statistical time series analysis and dimensionality reduction. We will discuss the differences between first principle models and data-driven models in real-time operations. Discussions and computer-based simulation projects will prepare the participants to understand better how to integrate data-driven and physics-based reasoning in modern power systems. It is ideally suited for those who work in areas associated with the electric grid and need to better understand the latest advance in data sciences and machine learning and how their work might be affected by this change.
|Grid Operation Basics
Intro to Data Availability in Power Systems
|High Dimensional Space|
|Singular Value Decomposition (SVD)|
|Application of SVD in Power System Anomaly Detection|
|Application of SVD in Bad Data Processing for State Estimation|
Application of learning in smart meter data
Statistical Time Series
|Application of Time Series Analysis in Renewable Forecasting|
|Application of Time Series Analysis in Distribution Systems
Le Xie is a Professor Professor and Chancellor EDGES Fellow in the Department of Electrical and Computer Engineering at Texas A&M University. He is also the Assistant Director of Texas A&M Energy Institute. He received B.E. in Electrical Engineering from Tsinghua University in 2004, S.M. in Engineering Sciences from Harvard in 2005, and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon in 2009. His industry experience includes ISO-New England and Edison Mission Energy Marketing and Trading. His research interest includes modeling and control in data-rich large-scale systems, grid integration of clean energy resources, and electricity markets.
Dr. Xie received the U.S. National Science Foundation CAREER Award, and DOE Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award. He was awarded the 2017 IEEE PES Outstanding Young Engineer Award. He was recipient of Texas A&M Dean of Engineering Excellence Award, ECE Outstanding Professor Award, and TEES Select Young Fellow. He is an Editor of IEEE Transactions on Smart Grid, and the founding chair of IEEE PES Subcommittee on Big Data & Analytics for Grid Operations. He and his students received the Best Paper awards at North American Power Symposium 2012, IEEE SmartGridComm 2013, and HICSS 2019. He chaired the 2018 NSF Workshop on Real-time Learning and Decision Making in Dynamical Systems.
Yannan Sun Dr. Yannan Sun is a data scientist at Oncor Electric Delivery. She received the B.S. degree in mathematics from the University of Science and Technology of China, Hefei, China, in 2004, and the M.S. degree in statistics and the Ph.D. degree in mathematics from Washington State University, Pullman, WA, USA, in 2007 and 2010, respectively. She was with the Pacific Northwest National Laboratory, Richland, WA, USA, from 2010 to 2017. Her research interests include multivariate risk (multivariate extremes and risk measures), statistical modeling, experimental design and simulation, and applying mathematical and statistical methods in power engineering and environmental science. She is the Vice Chair of the IEEE PES Subcommittee on Big Data & Analytics for Grid Operations.
Dileep Kalathil is an Assistant Professor in the Electrical and Computer Engineering Department at Texas A&M. Before joining Texas A&M, he was a postdoctoral scholar in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his PhD from the University of Southern California (USC) in 2014 where he won the best PhD Dissertation Prize in the USC Department of Electrical Engineering. He received an M.Tech from IIT Madras where he won the award for the best academic performance in the EE department. His research interests include control theory, sequential learning, game theory, and sustainable energy systems.
The virtual course will be administered through Texas A&M Engineering Experiment Station (TEES).
Cost: $1500/person if registered individually at full price;
$1200/person if more than 15 people registered from an organization.
Discount Code is provided for Smart Grid Center Membership (25% discount).
For More Information
- For more information about this course, or other upcoming Texas A&M electric power short courses contact Le Xie at email@example.com