Leveraging AMI data for distribution system model calibration and situational awareness

doi:10.1109/TSG.2014.2385636

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

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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:

Peppanen, Jouni ^[1]; Reno, Matthew J. ^[1]; Thakkar, Mohini ^[1]; Grijalva, Santiago ^[1]; Harley, Ronald G. ^[1]

Georgia Inst. of Technology, Atlanta, GA (United States)

Publication Date:: 2015-01-15

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.

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                    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.  doi:10.1109/TSG.2014.2385636.

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                    Peppanen, Jouni, Reno, Matthew J., Thakkar, Mohini, Grijalva, Santiago, and Harley, Ronald G. Thu .  
        "Leveraging AMI data for distribution system model calibration and situational awareness".  United States.  doi:10.1109/TSG.2014.2385636.  https://www.osti.gov/servlets/purl/1237701.

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@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},

  journal      = {IEEE Transactions on Smart Grid},

  issn         = {1949-3053},

  number       = 4,

  volume       = 6,

  place        = {United States},

  year         = {2015},

  month        = {1}

}

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DOI: 10.1109/TSG.2014.2385636

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