Monitoring of low voltage grids with multilayer principal component analysis

Abstract

This article presents a monitoring strategy based on multilayer principal component analysis (PCA) to detect and diagnose power system disturbances in large amounts of data collected by intelligent electronic devices in low voltage smart grids. The PCA models are built on multiple sliding windows, sized (in terms of length and sampling time) according to the type of phenomena to detect. Abnormalities are detected with use of two complementary statistical indexes, then diagnosed by computing the individual contributions of each monitored variable to the constraint violation of those statistics. As a result, its implementation enables an automatic analysis of multiple phenomena of interest in parallel over time using distinct electrical quantities. Furthermore, the method is demonstrated within the RESOLVD project with data from the OpenLV project containing measurements of active and reactive power gathered at different low voltage distribution substations

This work has been supported by the European Union’s Horizon 2020 research and innovation framework under the auspices of the project Renewable penetration levered by Efficient Low Voltage Distribution grids, grant agreement number 773715, and University of Girona scholarship