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In the monitoring of a complex electric grid, it is of paramount importanceto provide operators with early warnings of anomalies detected on the network,along with a precise classification and diagnosis of the specific fault type.In this paper, we propose a novel multi-stage early warning system prototypefor electric grid fault detection, classification, subgroup discovery, andvisualization. In the first stage, a computationally efficient anomalydetection method based on quartiles detects the presence of a fault in realtime. In the second stage, the fault is classified into one of nine pre-defineddisaster scenarios. The time series data are first mapped to highlydiscriminative features by applying dimensionality reduction based on temporalautocorrelation. The features are then mapped through one of threeclassification techniques: support vector machine, random forest, andartificial neural network. Finally in the third stage, intra-class clusteringbased on dynamic time warping is used to characterize the fault with furthergranularity. Results on the Bonneville Power Administration electric grid datashow that i) the proposed anomaly detector is both fast and accurate; ii)dimensionality reduction leads to dramatic improvement in classificationaccuracy and speed; iii) the random forest method offers the most accurate,consistent, and robust fault classification; and iv) time series within a givenclass naturally separate into five distinct clusters which correspond closelyto the geographical distribution of electric grid buses.

Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning