Learn more about the U.S. Department of Energy FOA 1861 projects: https://www.energy.gov/articles/department-energy-announces-20-million-artificial-intelligence-research. Full abstract and bios can be found on the NASPI website: www.naspi.org
GE will discuss their application of a powerful, industry-validated signature identification strategy to the FOA 1861 PMU dataset. 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.
Schweitzer Engineering Laboratories (SEL), in partnership with Oregon State University (OSU), has developed novel data handling, data anomaly mitigation, and event classification techniques using the FOA 1861 dataset. The result of this joint research effort is an array of techniques that are applicable to every stage of the machine learning pipeline, from preprocessing to event detection. This research contributes directly to the goal of providing situational awareness using real phasor measurement unit data in an online environment such as Synchrowave Operations.
Siemens will present the innovative solutions associated with the application of ML methods to the FOA 1861 PMU dataset that resulted from their MindSynchro project. Challenges concerning the relevance of adequately labeling PMU data for proper extraction of its value and the novel approaches developed for overcoming existing limitations in available labels as well as the adequacy of the developed solutions for real world application will be discussed.
The University of Nevada, Reno will present the findings from their project which focused on developing a robust event diagnostics platform by integrating state-of-the-art tensor analytics and machine learning into real-time grid monitoring.
Download flyer here: https://www.naspi.org/sites/default/files/2021-07/naspi_webinar_flyer_20210728_0.pdf