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Title: Phase identification using co-association matrix ensemble clustering

Journal Article · · IET Smart Grid
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; NA0003525; 34226
OSTI ID:
1742049
Alternate ID(s):
OSTI ID: 1634795; OSTI ID: 1786774
Report Number(s):
SAND-2020-5476J; 686330
Journal Information:
IET Smart Grid, Vol. 3, Issue 4; ISSN 2515-2947
Publisher:
The Institution of Engineering and TechnologyCopyright Statement
Country of Publication:
United States
Language:
English

References (14)

Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness journal July 2015
An approach for assessing high-penetration PV impact on distribution feeders journal April 2016
Unbalanced multi-phase distribution grid topology estimation and bus phase identification journal December 2019
Spectral Clustering Ensemble Applied to SAR Image Segmentation journal July 2008
High PV penetration impacts on five local distribution networks using high resolution solar resource assessment with sky imager and quasi-steady state distribution system simulations journal July 2016
A Cloud-Based Black-Box Solar Predictor for Smart Homes journal October 2017
Improving Supervised Phase Identification Through the Theory of Information Losses journal May 2020
A Survey of Clustering Ensemble Algorithms journal May 2011
Phase Identification With Incomplete Data journal July 2018
A survey on Advanced Metering Infrastructure journal December 2014
Distributed Energy Resources Topology Identification via Graphical Modeling journal July 2017
Advanced Metering for Phase Identification, Transformer Identification, and Secondary Modeling journal June 2013
Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data journal September 2018
A Data-Driven Approach for Detection and Estimation of Residential PV Installations journal September 2016

Figures / Tables (17)

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
pattern clustering
learning (artificial intelligence)
calibration
smart meters
time series
matrix algebra
model calibration tasks
phase identification task
spectral clustering approach
hosting capacity analysis
recent availability
smart meter data
co-association matrix-based
distributed energy resources
machine learning tools
existing phase labels
accurate phase labels
calibrating distribution system models
phase identification research
synthetic data