Course Director: Professor Le Xie
Next Offering: Dates to be announced.
The course is designed to provide introductory coverage 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. 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.
(Hours: CEU 1.8, PDH 18).
Who Should Attend
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
- Neural Nets
- Machine Learning
- Application of Learning in Smart Meter Data
- Reinforcement Learning
- Statistical Time Series
- Application of Time Series Analysis in Renewable Forecasting
- Application of Time Series Analysis in Distribution Systems
- Model Identification
Le Xie is a Professor Professor and Eugene Webb Faculty 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 has an MS in Statistics and a Ph.D. in Math from Washington State University. She is currently a Data Scientist in the Maintenance Strategy and Technical Support Group at Oncor, where she leverages her advanced data science knowledge to support the development of maintenance strategies and program impact analysis. Prior to Oncor, she worked seven years as a Senior Scientist at Pacific Northwest National Laboratory (PNNL) in the Electricity Infrastructure group. Her expertise lies in data analytics and machine learning using power system data, which she has employed to develop many data-driven algorithms for load forecasting, voltage anomaly detection, state estimation and equipment preventive maintenance. Dr. Sun has received the Spirit of Innovation Award at Oncor and Outstanding Performance Award from several projects at PNNL. She is also the Vice Chair and Technical Committee Program 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.
Location: Texas A&M Center for Infrastructure Renewal (CIR), 1041 RELLIS Parkway, Bryan TX
Directions & map: CIR Directions
Direct flights available at the Texas A&M Easterwood Airport (CLL) from Dallas-Fort Worth (DFW) on American and Houston (IAH) on United.
Other airports within driving distance: Austin (AUS) and Houston (IAH), both about two hours away
May rent car or use shuttle service: Airport Ground Shuttle
For More Information
- For more information about this course, or other upcoming Texas A&M electric power short courses contact Le Xie at firstname.lastname@example.org