Leveraging AMI data for distribution system model calibration and situational awareness
Abstract
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.
- Authors:
-
- Georgia Inst. of Technology, Atlanta, GA (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1237701
- Report Number(s):
- SAND-2015-7431J
Journal ID: ISSN 1949-3053; 603405
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- IEEE Transactions on Smart Grid
- Additional Journal Information:
- Journal Volume: 6; Journal Issue: 4; Journal ID: ISSN 1949-3053
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; graphical user interfaces; load modeling; parameter estimation; power distribution; power system measurements; smart grids; state estimation; visualization
Citation Formats
Peppanen, Jouni, Reno, Matthew J., Thakkar, Mohini, Grijalva, Santiago, and Harley, Ronald G. Leveraging AMI data for distribution system model calibration and situational awareness. United States: N. p., 2015.
Web. doi:10.1109/TSG.2014.2385636.
Peppanen, Jouni, Reno, Matthew J., Thakkar, Mohini, Grijalva, Santiago, & Harley, Ronald G. Leveraging AMI data for distribution system model calibration and situational awareness. United States. https://doi.org/10.1109/TSG.2014.2385636
Peppanen, Jouni, Reno, Matthew J., Thakkar, Mohini, Grijalva, Santiago, and Harley, Ronald G. 2015.
"Leveraging AMI data for distribution system model calibration and situational awareness". United States. https://doi.org/10.1109/TSG.2014.2385636. https://www.osti.gov/servlets/purl/1237701.
@article{osti_1237701,
title = {Leveraging AMI data for distribution system model calibration and situational awareness},
author = {Peppanen, Jouni and Reno, Matthew J. and Thakkar, Mohini and Grijalva, Santiago and Harley, Ronald G.},
abstractNote = {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.},
doi = {10.1109/TSG.2014.2385636},
url = {https://www.osti.gov/biblio/1237701},
journal = {IEEE Transactions on Smart Grid},
issn = {1949-3053},
number = 4,
volume = 6,
place = {United States},
year = {Thu Jan 15 00:00:00 EST 2015},
month = {Thu Jan 15 00:00:00 EST 2015}
}
Web of Science
Works referencing / citing this record:
Survey of wireless big data
journal, March 2017
- Qian, Lijun; Zhu, Jinkang; Zhang, Sihai
- Journal of Communications and Information Networks, Vol. 2, Issue 1
Topology-Based Estimation of Missing Smart Meter Readings
journal, January 2018
- Kodaira, Daisuke; Han, Sekyung
- Energies, Vol. 11, Issue 1
Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping
journal, May 2018
- Jiang, Zhen; Shi, Di; Guo, Xiaobin
- Energies, Vol. 11, Issue 6
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- Huang, Xiaoyao; Hu, Tianbin; Ye, Chengjin
- Energies, Vol. 12, Issue 4
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journal, July 2019
- Liu, Yaoxian; Sun, Yi; Li, Bin
- Information, Vol. 10, Issue 7