Real-time Anomaly Detection and Classification in Streaming PMU Data

TL;DR

This paper introduces an interpretable, real-time framework for detecting and classifying anomalies in streaming PMU data, aiding power grid operators in quick decision-making and system understanding.

Contribution

It presents a novel, interpretable statistical learning framework for real-time anomaly detection and classification in streaming power grid data.

Findings

Effective dynamical model learned from real PMU data

Accurate real-time anomaly detection using probabilistic predictions

Successful classification of anomalies into common categories

Abstract

Ensuring secure and reliable operations of the power grid is a primary concern of system operators. Phasor measurement units (PMUs) are rapidly being deployed in the grid to provide fast-sampled operational data that should enable quicker decision-making. This work presents a general interpretable framework for analyzing real-time PMU data, and thus enabling grid operators to understand the current state and to identify anomalies on the fly. Applying statistical learning tools on the streaming data, we first learn an effective dynamical model to describe the current behavior of the system. Next, we use the probabilistic predictions of our learned model to define in a principled way an efficient anomaly detection tool. Finally, the last module of our framework produces on-the-fly classification of the detected anomalies into common occurrence classes using features that grid operators are familiar with. We demonstrate the efficacy of our interpretable approach through extensive numerical experiments on real PMU data collected from a transmission operator in the USA.