  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.
