Smart grid line event classification using supervised learning over PMU data streams

Abstract

Emerging smart grid technology offers the possibility of a more reliable, efficient, and flexible energy infrastructure. A core component of the smart grid are phasor measurement units (PMUs) which offer the ability to capture time-coherent measurements across a geographically distributed area. However, due to the fast sampling rate of these devices, a significant volume of data is generated on a daily basis and this presents challenges for how to leverage the information most effectively. In this paper, we address this challenge by applying machine learning techniques to PMU data for the purpose of detecting line events in a wide-area power grid. Specifically, we use archived synchrophasor data from PMUs located across the Pacific Northwest to train and test a decision tree built using the J48 algorithm. In contrast to other studies exploring machine learning in the context of the smart grid, our work uses PMU data from a large, active, power grid as opposed to data obtained from a simulation. We show that our classifier performs as well as hand-coded rules developed by a domain expert when applied at locations near to a line fault and that it significantly outperforms hand-coded rules when identifying line faults from a distance.