Phase identification using co-association matrix ensemble clustering

IET Smart Grid

Matthew J. Reno¹  Logan Blakely¹ 

[1] Sandia National Laboratories;

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

DOI  :  10.1049/iet-stg.2019.0280

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown