Abstract
Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large-scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra-high density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication.
| Original language | English |
|---|---|
| Pages (from-to) | 924-937 |
| Number of pages | 14 |
| Journal | High Voltage |
| Volume | 6 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 2021 |
| Externally published | Yes |
Funding
This work was supported in part by the U.S. Department of Energy Solar Energy Technologies Office under Award 34231 and 34224. This work was supported in part by NSF EAGER: Program under award number 1839684 and Cyber‐Physical Systems (CPS) Program under award number 1931975. This work also made use of Engineering Research Center shared facilities supported by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC‐1041877 and the CURENT Industry Partnership Program. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE‐AC36‐08GO28308. This manuscript was also authored in part by Oak Ridge National Laboratory, operated by UT‐Battelle, LLC under Contract No. DE‐AC05‐00OR22725 with the U.S. Department of Energy. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non‐exclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. This work was supported in part by the U.S. Department of Energy Solar Energy Technologies Office under Award 34231 and 34224. This work was supported in part by NSF EAGER: Program under award number 1839684 and Cyber-Physical Systems (CPS) Program under award number 1931975. This work also made use of Engineering Research Center shared facilities supported by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. This manuscript was also authored in part by Oak Ridge National Laboratory, operated by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.