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Title: Considerations for AMI-Based Operations for Distribution Feeders: Preprint

Abstract

More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of thismore » study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.« less

Authors:
 [1];  [1];  [2];  [1];  [3];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Florida
  3. San Diego Gas & Electric Company
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1569442
Report Number(s):
NREL/CP-5D00-72773
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 IEEE Power & Energy Society General Meeting (IEEE PES GM), 4-8 August 2019, Atlanta, Georgia
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; advanced metering infrastructure; correlation coefficient; phase identification; power distribution lines; regression analysis

Citation Formats

Padullaparti, Harsha Vardhana, Veda, Santosh, Dhulipala, Surya, Baggu, Murali M, Bialik, Tom, and Symko-Davies, Martha. Considerations for AMI-Based Operations for Distribution Feeders: Preprint. United States: N. p., 2019. Web.
Padullaparti, Harsha Vardhana, Veda, Santosh, Dhulipala, Surya, Baggu, Murali M, Bialik, Tom, & Symko-Davies, Martha. Considerations for AMI-Based Operations for Distribution Feeders: Preprint. United States.
Padullaparti, Harsha Vardhana, Veda, Santosh, Dhulipala, Surya, Baggu, Murali M, Bialik, Tom, and Symko-Davies, Martha. Mon . "Considerations for AMI-Based Operations for Distribution Feeders: Preprint". United States. https://www.osti.gov/servlets/purl/1569442.
@article{osti_1569442,
title = {Considerations for AMI-Based Operations for Distribution Feeders: Preprint},
author = {Padullaparti, Harsha Vardhana and Veda, Santosh and Dhulipala, Surya and Baggu, Murali M and Bialik, Tom and Symko-Davies, Martha},
abstractNote = {More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of this study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
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