Phase identification using co-association matrix ensemble clustering

Journal Article · 01 June 2020 · IET Smart Grid

DOI: https://doi.org/10.1049/iet-stg.2019.0280 · OSTI ID:1634795

Blakely, Logan ^[1]; Reno, Matthew J. ^[1]

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1. 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