Synchrophasor data analytics in distribution grids

The deployment of high-fidelity, high-resolution sensors in distribution systems will play a key role in enabling increased resiliency and reliability in the face of a changing generation landscape. In order to leverage the full potential of such a rich dataset, it is necessary to develop an analytics framework capable of both detecting and analyzing patterns within events of interest. This work details the foundation of such an infrastructure. Here, we present an algorithm for detecting events, in the form of edges in voltage magnitude time series data, and an approach for clustering sets of events to reveal unique features that distinguish different events from one another (e.g. capacitor bank switching from transformer tap changes). We test the proposed infrastructure on distribution synchrophasor data obtained from a utility in California over a one week period. Our results indicate that event detection and clustering of archived data reveals features unique to the operation of voltage regulation equipment. The chosen data set particularly highlights the value of the derivative of the localized voltage angle as a distinguishing feature.