Phase identification using co‐association matrix ensemble clustering. Issue 4 (23rd June 2020)

• Record Type: Journal Article
• Authors: Blakely, Logan; Reno, Matthew J.
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 490
• Page End: 499
• Publication Date: 2020-06-23
• Subjects: 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; C1140Z Other topics in statistics -- C6170K Knowledge engineering techniques -- C7330 Biology and medical computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0280
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.