IEEE PES ​​Subcommittee on Big Data & Analytics for Power Systems IEEE


Title: Big Data Access, Analytics and Sense-Making

Time: Oct 30, 2018 at 2:00 PM EST (1:00 PM CST, 12:0 0 PM MST or 11:00 AM PST)

Abstract: Modern power systems have more and better sensors for observing and optimizing their system performance and economics. These sensors generate vastly larger volumes of data of various types and speeds that need to be handled efficiently and effectively for driving meaningful analytics. To compound this challenge, large-scale analytics involves modeling and simulation that generate even more data. All these data eventually need to be converted into knowledge and information that make sense to operators and planners, so actions could be taken to manage the complexity of power systems. This seminar will put the power system data challenge into perspectives compared with other domains and will look at borrowing techniques from other domains to enable large-scale data access, analytics and sense-making. The techniques include those in the areas of mathematics, computing and visualization. Tools and resources (some publicly accessible) will be discussed as examples of approaches to tackle the big data challenge in power systems.

Bio: Zhenyu (Henry) Huang (M’01 SM’05 F’17 IEEE) received his B. Eng. from Huazhong University of Science and Technology, Wuhan, China, and Ph.D. degree from Tsinghua University, Beijing, China, in 1994 and 1999, respectively. From 1998 to 2003, he conducted extensive research at the University of Hong Kong, McGill University (Canada), and the University of Alberta (Canada). He is currently Laboratory Fellow and Technical Group Manager at Pacific Northwest National Laboratory, Richland, Washington, USA. Dr. Huang has over 140 peer-reviewed publications. His research interests include high performance computing, data analytics, and optimization and control for power systems and other related infrastructures. Dr. Huang is a Fellow of IEEE and active in several IEEE Power and Energy Society (PES) technical committees. He led the Richland Chapter to win the 2007 IEEE PES Outstanding Small Chapter Award. He is the recipient of the 2008 PNNL Ronald L. Brodzinski’s Award for Early Career Exceptional Achievement and the 2009 IEEE Power and Energy Society Outstanding Young Engineer Award. Dr. Huang is a registered Professional Engineer in Washington State.


Title: Sacramento Municipal Utility District (SMUD)’s Data Analytics Initiatives

Time: November 28, 2017 at 2:00 PM EST

Abstract: SMUD implemented $350 million worth of smart grid projects that resulted in dramatic increases in the amount of available data. SMUD used the data to implement several initiatives in the areas of workforce and asset management, operational efficiencies, distributed energy resource management and customer programs. SMUD continues to use the data to improve grid performance and to develop programs focused on the customer side—SMUD’s customer analytics team worked on a load disaggregation model to identify customers with large HVAC usage, base loads and variable loads. The model empowered three data products: 1) a rank list of high potential customers who can benefit from SMUD’s offerings; 2) an internal app for energy auditors to provide personalized educational talking points when conduct audits; and 3) personalized marketing messages. By providing relevant and personalized messages and offerings, SMUD is building a better customer experience.

Jim Parks is a program manager in the Energy Research and Development department at the Sacramento Municipal Utility District (SMUD). He just completed a $308 million smart grid initiative (SmartSacramento®) with over 40 individual projects ranging from smart meters and distribution automation to customer programs including demand response and energy efficiency. He currently oversees energy efficiency and smart grid R&D projects. Prior to his current assignment, he worked with emerging energy efficiency technologies, electric transportation, energy efficiency program development, energy efficiency program operations and transmission planning.
Yifan Lu is a project manager leading the customer analytic team at SMUD.


Title: Application of Machine Learning to Power Grid Analysis

Time: November 21, 2017 at 8:00 PM EST

Abstract: After the AlphaGo winning game, there is a renewed interest in artificial intelligence, especially in the area of Machine Learning, in the power engineering community. This Webinar will give a general overview of Machine Learning and its potential application to power grid analysis. It will include two parts. In the first part, an open platform for exploring the application of Machine Learning to power grid analysis will be introduced, including the discussion of its architecture, and some sample application scenarios. The platform is based on the integration of InterPSS, an open source power grid simulation software project, and TensorFlow, Google’s Machine Learning engine; In the second part, some of our recent on-going research work in the area of applying Machine Learning to perform fast power system dynamic security assessment (DSA) will be presented, including some preliminary results of the
application of Machine Learning to a large-scale power network for the DSA analysis.

Mike Zhou (M’90) obtained his B.S. from Hunan University, M.S. and PhD. from Tsinghua University. He was an assistant professor at University of Saskatchewan 1990-1992, served as the VP in charging of power distribution system software development at EDSA Micro Corporation 1992-1997. He was a Senior Computer System Architect with TIBCO Software Inc. 2000-2014. He joined State Grid Electric Power Research Institute of China as a Chief Scientist in 2014, sponsored by the “Thousand Talents Plan” program. His current research interests include Big Data and Machine Learning technologies, and their application to large-scale power grid on-line analysis.


Title: An energy IoT platform for real-time production and delivery of wind power generation forecasts

Webinar Address: Link

Time: June 28, 2017 at 2:00 PM EST (1:00 PM MST or 11:00 AM PST)

Abstract: Power generation using renewable energy resources such as wind turbines has grown increasingly popular. Because the underlying meteorological processes are highly unpredictable, it has become important to be able to provide accurate power forecasts in real-time. In this talk we will describe an end-to-end IoT platform that enables SCADA sensor data to be collected in real-time directly from a remote wind farm, securely and reliably transmitted to cloud servers where data is analyzed to create forecasting models. These models are then applied to the turbine sensor data stream to generate day-ahead power generation forecasts. We will also describe the machine learning techniques used as the basis for the forecasting models and our strategies to make the solution scalable for other big data applications.

Bio: Chandrasekar (Chandra) Venkatraman is Principal Research Scientist at Hitachi America Research and Development in the Big Data Laboratory focusing on Industrial IoT Architectures and Analytics for Energy. Prior to joining he was Chief Scientist at FogHorn Systems – Palo Alto based start-up focusing on Big Data Analytics and applications platform for Industrial Internet of Things (IoT). Chandra was with Hewlett Packard Labs, Palo Alto for almost two decades working on Information architectures, distributed computing, in-home network, ePrint architecture, sensor networks and Internet of Things. He has authored over 15 patents and a number of research papers and talks.

Pierre Huyn has over 30 years of research and advanced development experience in data management, big data analytics, and software engineering. His current interest is in big data architectures for IoT and deep learning for time series data in the domain of renewable energy.

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