Phasor measurement units (PMUs) have been deployed at over 2,500 locations across the nation’s bulk power systems. Due to these PMU measurements, grid owners and operators now possess unprecedented quantities of data detailing the condition of the grid. Robust tools are needed for the analysis and discovery of the information hiding within the growing archives of PMU data, so future PMUs can contribute even more to the efficient, safe, and reliable operation and design of the nation’s electric system.

In 2019, the Department of Energy (DOE) selected eight projects to explore the use of big data, artificial intelligence (AI), and machine-learning technology and tools on PMU data to identify and improve existing knowledge, and to discover new insights and tools for better grid operation and management.

For more information, please view  this press release and fact sheet.

The projects received a first-of-its-kind dataset that:

  • Covered a significant number of PMUs and substations in each of the three U.S. interconnections;
  • Covered multiple years and included event logs;
  • Consisted of real field data from a variety of sources that includes errors, inconsistencies, quality levels, and flaws; and
  • Was anonymized to reduce the possibility of exposing potential information about power system operational vulnerabilities.

A subset of this data is publicly available via OE’s Grid Event Signature Library ( The subset is stored under Provider 9 and Provider 10. 

A subset of this data is publicly available via OE’s Grid Event Signature Library ( The subset is stored under Provider 9 and Provider 10. 

The projects have been completed and below are their final reports along with links to some of the publications supported through this research and related presentations. After the completion of the projects, PNNL conducted a meta-analysis of the projects and created a report that summarizes the key findings across the projects. 

Table of Awardees

Prime Recipient Team Members Project Title
Iowa State University of Science & Technology Electric Power Group, Google Brain, IBM Robust Learning of Dynamic Interactions for Enhancing Power System Resilience
Schweitzer Engineering Laboratories, Inc. Oregon State University Machine Learning Guided Operational Intelligence from Synchrophasors
The Regents of the University of California Electric Power Group, Michigan Technological University Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks
Board of Regents, NSHE obo University of Nevada, Reno Arizona State University, IBM, Virginia Tech A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning Into Real-time Grid Monitoring
General Electric Company GE Grid Solutions PMU-Based Data Analytics using Digital Twin and PhasorAnalytics Software
Siemens Corporation, Corporate Technology Siemens Digital Grid, Siemens Industries and Drives (Mindsphere), Southern Methodist University, Temple University MindSynchro
Ping Things, Inc. N/A Combinatorial Evaluation of Physical Feature Engineering and Deep Temporal Modeling for Synchrophasor Data at Scale
Texas A&M Engineering Experiment Station Temple University, Quanta Technology LLC, OSIsoft, LLC Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

Final Reports and Publications

A summary analysis of the findings in these awards was conducted by PNNL. This report also contains full lists of publications from the awards.  

Reports and publications are publicly available via OSTI links below:  

As part of assembling the data sets and supporting the awardees, PNNL published the following reports and presentations, primarily focused on the challenges faced when dealing with a big-data curation effort, opportunities to use the data for research purposes, and outcomes of the research by the awardees. The publications are listed chronologically in the table below, with clickable links to take you to the publication location. 

Title Date Type
Data for FOA 1861 Bringing It All Together (PNNL-SA-161370)  4/14/2021 Presentation
FOA 1861 Data Curation Overview (PNNL-32303)  11/12/2021 Report
FOA 1861 - A Precursor to New Research (PNNL-SA-172079)  4/12/2022 Presentation
Big Data Analysis of Synchrophasor Data: Outcomes of Research Activities Supported by DOE FOA 1861 (PNNL-33548)  10/17/2022 Report
DOE FOA 1861 Research Outcomes (PNNL-SA-178557)  10/18/2022 Presentation

Related Publications and Presentations

If you have any questions regarding the data used for these projects, please contact Jim Follum at

Page updated as of March 21, 2023.