The use of machine learning and AI in analysis of historical and on-line synchrophasor data
Date: Tuesday, May 25 Time:
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Organisation: Texas A&M University, USA
Short biography of the chair: Mladen Kezunovic (S’77–M’80–SM’85–F’99– LF’17) received the Dipl. Ing. from University of Sarajevo, Sarajevo, Bosnia, and M.Sc. and Ph.D. degrees in electrical engineering from University of Kansas, Lawrence, KS, in 1974, 1977, and 1980, respectively. He has been with Texas A&M University, College Station, TX, USA since 1986, where he is currently Regents Professor, Eugene E. Webb Professor, and the Site Director of “Power Engineering Research Center” consortium. For over 25 years he has been the Principal Consultant of XpertPower Associates, a consulting firm specializing in power systems data analytics. His expertise is in protective relaying, automated power system disturbance analysis, computational intelligence, data analytics, and smart grids. He has authored over 600 papers, given over 120 seminars, invited lectures, and short courses, and consulted for over 50 companies worldwide. Dr. Kezunovic is a CIGRE Fellow, Honorary and Distinguished member. He is Registered Professional Engineer in Texas.
Abstract: The synchrophasor network in the USA has been expanding over the last decade and has reached over 3000 PMUs installed in the three interconnections: The West, East and ERCOT. The Transmission Operators and Independent System Operators have collected large amounts of historical PMU data. The challenges in searching through the data, detecting events of interest, an classifying them have been recognized. The Department of Energy has funded eight on-going projects to develop Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze and classify the events automatically. This panel will be focusing on sharing the experiences from four projects that have focused on different issues associated with the automation of synchrophasor data analytics: data wrangling, bad data detection and removal, reconstruction of missing data, data mislabeling, supervised, semi-supervised, unsupervised and transfer learning, deep learning, etc.
Name: Philip Hart
Organisation: GE Research, USA
Short biography: Philip J. Hart (S’13–M’18) received the B.Sc. degree (Hons.) in electrical engineering from Clarkson University, Potsdam, NY, USA, in 2011, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Wisconsin–Madison, Madison, WI, USA, in 2013 and 2017, respectively.,He was a Research Assistant with the Wisconsin Electric Machines and Power Electronics Consortium (WEMPEC). He is currently a Lead Electric Power Systems Engineer with GE Global Research, Niskayuna, NY, USA. His research interests include power systems engineering, microgrids, grid-tied power electronics, and power systems cybersecurity. His PhD work focused on the modeling and analysis of next-generation power systems composed of power-electronics-based resources such as solar, wind and battery energy storage. He has researched advanced microgrid control techniques that address network voltage and frequency regulation, and optimal control of microgrid resources. He has practical experience in validating microgrid controller systems in hardware, using experimental testbeds and real-time hardware-in-the-loop platforms.
Title of presentation: PMU-Based Data Analytics Using Digital Twin and Phasor Analytics Software
Abstract: Application of a powerful, industry-validated signature identification strategy to a large PMU dataset is described. The event signatures generated using the semi-supervised strategy are derived from an over-abundance of features calculated in a transparent manner, and can be efficiently applied to either historical or streaming PMU data. The signatures can be used to quantify the relative severity, location, and duration of each event, and show promise for integration into tools that provide enhanced power systems reliability, operational efficiency and resiliency.
Name: Bruno Leao
Organisation: Siemens Technology, USA
Short biography: Bruno Leao is a Data Scientist with Siemens, CT, Princeton, New jersey. Bruno Leão received the B.S. degree in control and automation engineering from the Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, in 2004, and the M.S. degree in aeronautical engineering and D.Sc. degree in electronics engineering and computer science from the Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos, Brazil, in 2007 and 2011, respectively. He was with Embraer S.A., Brazil, São José dos Campos, from 2005 to 2012. He has been with the PHM Research Group, Embraer, where he has been researching PHM solutions for aircraft systems. He was a System Engineer in flight controls and automatic flight controls
Title of presentation: MindSynchro: innovation and challenges in application of ML to PMU big data
Abstract: The presentation describes recently developed innovations associated to the application of ML methods to real world PMU big data. Flagship innovation comprises semi-supervised learning approaches for detection of relevant grid events which can be applied to a very broad PMU fleet. Other innovations and challenges are also discussed, especially concerning the relevance of adequately labeling PMU data for proper extraction of its value and approaches for overcoming existing limitations in such information.
Name: Zoran Obradovic
Organisation: Temple University – L.H. Carnell Professor, USA
Short biography: Zoran Obradovic is a L.H. Carnell Professor of Data Analytics at Temple University, Professor in the Department of Computer and Information Sciences with a secondary appointment in Statistics, and is the Director of the Center for Data Analytics and Biomedical Informatics. He is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at eleven journals. He is the chair at the SIAM Activity Group on Data Mining and Analytics for 2014 and 2015 years, was co-chair for 2013 and 2014 SIAM International Conference on Data Mining and was the program or track chair at many data mining and biomedical informatics conferences. His work is published in more than 300 articles and is cited more than 15,000 times (H-index 48). In 2015, he became an elected member of Academia Europaea (the Academy of Europe).
Title of presentation: BDSmart: Automated Analysis of Large synchrophasor datasets
Abstract: We will discuss tradeoffs in machine learning and AI applications to automated PMU data analysis: preprocessing vs raw data, feature engineering vs inaccurate labels, computational implicity vs complexity, supervised vas unsupervised learning. We will then present some comparative results from different algorithms adopted by us to the problem at hand. Some lessons learned will be shared and discussed.
Name: Nanpeng Yu
Organisation: University of California, Riverside, USA
Short biography: Dr. Yu received his B.S. in Electrical Engineering from Tsinghua University, Beijing, China, in 2006. Dr. Yu also received his M.S. degrees in Electrical Engineering and Economics and Ph.D. degree from Iowa State University in 2010. Before joining University of California, Riverside, Dr. Yu was a senior power system planner and project manager at Southern California Edison from Jan, 2011 to July 2014. Currently, he is an associate professor of Electrical and Computer Engineering at the University of California, Riverside, CA. Dr. Yu is the recipient of the Regents Faculty Fellowship and Regents Faculty Development award from University of California. He received multiple best paper awards form IEEE Power and Energy Society Grand International Conference and Exposition Asia and the Second International Conference on Green Communications, Computing and Technologies. Dr. Yu also received three best paper finalist awards from IEEE Power and Energy Society General Meeting. Dr. Yu is the director of Smart City Innovation Laboratory at UC Riverside. Dr. Yu is also a cooperating faculty member of department of computer science and engineering, department of Statistics and Center for Environmental Research & Technology. He currently serves as the vice chair distribution system operation and planning subcommittee of IEEE Power and Energy Society and the co-chair for IEEE Big Data Applications in Power Distribution Networks Task Force. Dr. Yu currently serves as the associate editor for IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, and International Transactions on Electrical Energy Systems.
Title of presentation: Physics-Informed Machine Learning for Power System Event Detection and Identification with Synchrophasor Data
Abstract: Three recent breakthroughs in physics-informed synchrophasor data analytics will be reported in this panel presentation. First, we will talk about how to construct a graph Laplacian using off-line training data and detect power system events using graph signal processing techniques. Second, we will talk about how to leverage deep neural networks enhanced augmented by information loading and graph-based sorting to classify power system events based on PMU data.