Open AccessPhase identification using co‐association matrix ensemble clustering
Author(s) -
Blakely Logan,
Reno Matthew J.
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0280
Subject(s) - cluster analysis , computer science , identification (biology) , task (project management) , data mining , ensemble learning , machine learning , calibration , smart meter , artificial intelligence , association (psychology) , energy (signal processing) , phase (matter) , matrix (chemical analysis) , smart grid , engineering , statistics , mathematics , botany , chemistry , organic chemistry , biology , philosophy , electrical engineering , systems engineering , epistemology , materials science , composite material
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.