Real-time Anomaly Detection and Classification in Streaming PMU Data

Phasor measurement units (PMUs) are being pervasively deployed in the grid to provide fast-sampled operational data to aid control and decision-making for reliable operation of the electric grid. This work presents a general and interpretable framework for analyzing PMU data in real-time. The proposed framework enables grid operators to understand changes to the current state and to identify anomalies in the PMU measurement data. We first learn an effective dynamical model to describe the current behavior of the system by applying statistical learning tools on the streaming PMU data. Next, we use the probabilistic predictions of our learned model to principally define an efficient anomaly detection tool. Finally, our framework produces real-time classification of the detected anomalies into common occurrence classes. We demonstrate the efficacy of our proposed framework through numerical experiments on real PMU data collected from a transmission operator in the USA.