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Title: Leveraging AMI data for distribution system model calibration and situational awareness

The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. Moreover, we demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.
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  1. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 1949-3053; 603405
Grant/Contract Number:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: 6; Journal Issue: 4; Journal ID: ISSN 1949-3053
Institute of Electrical and Electronics Engineers (IEEE)
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
24 POWER TRANSMISSION AND DISTRIBUTION; graphical user interfaces; load modeling; parameter estimation; power distribution; power system measurements; smart grids; state estimation; visualization
OSTI Identifier: