Big Data Analysis of Synchrophasor Data: Experience from the U.S. (Academic Track) (slides)

The U.S. Department of Energy (DOE) released $5.8 million funding to support the research and development (R&D) of advanced tools and controls that will improve the resilience and reliability of the national power grid. Eight project teams are selected to explore the use of big data, artificial intelligence (AI), and machine learning technology and tools to derive more value from the vast amounts of sensor data already being gathered and used to monitor the health of the grid and support system operations. This panel session serves as a forum for the four academic project teams to present their findings from mining terabytes of PMU data in the U.S. The team leads will also share their experience in analyzing the large-scale PMU data and developing useful tools and algorithms. The discussions and findings will help shape future development and application of faster grid analytics and modeling; better grid asset management; and sub-second automatic control actions that will help system operators avoid grid outages, improve operations, and reduce costs.