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Physics-Based Features for Anomaly Detection in Power Grids with Micro-PMUs

The expansion of monitoring systems for power grids from the traditional transmission lines to include the distribution grid - closer to the end-user - is largely due to the advance in electrical monitoring devices and their increasing affordability. This paradigm-shift benefits from new algorithms that can be implemented at the grid edge (as opposed to the transmission grid) to detect anomalies faster, more efficiently, and in real-time. Micro-PMUs are phasor measurement units that can be placed on the distribution lines to provide real-time data about the state of the grid and can detect fast transients. In this paper, we use data from micro-PMUs to engineer physics-based features for a novel anomaly detection algorithm that detects anomalies based on the transient properties of the power grid. This allows for a more capable, agile, and reliable anomaly scoring system that can learn normal behavior patterns and detect anomalous behavior efficiently. We show that by combining the classical data-based approaches with physics-based features in the anomaly detection algorithm, the machine learning algorithm proposed is capable of detecting different classes of anomalies (such as edge devices on-off attacks, single line-to-ground (SLG) faults, etc.). Simulation results on IEEE 34-node test feeder show that we can achieve better detection performance than traditional techniques for a broader class of anomalies.