Phase identification using co-association matrix ensemble clustering
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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 Laboratories (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
- OSTI ID:
- 1634795
- Alternate ID(s):
- OSTI ID: 1742049; OSTI ID: 1786774
- Report Number(s):
- SAND--2020-5476J; 686330
- Journal Information:
- IET Smart Grid, Journal Name: IET Smart Grid Journal Issue: 4 Vol. 3; ISSN 2515-2947
- Publisher:
- The Institution of Engineering and TechnologyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
pattern clustering
learning (artificial intelligence)
calibration
smart meters
time series
matrix algebra
model calibration tasks
phase identification task
time series
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
smart meters
calibrating distribution system models
phase identification research
synthetic data