Real-time Anomaly Detection and Classification in Streaming PMU Data
This work addresses the problem of ensuring secure and reliable power grid operations for system operators, representing an incremental improvement through a novel method for a known bottleneck.
The authors tackled real-time anomaly detection and classification in streaming power grid data by developing an interpretable framework that learns a dynamical model and uses probabilistic predictions for anomaly detection, demonstrating its efficacy on real PMU data from a US transmission operator.
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.