Multi-stage fault warning for large electric grids using anomaly detection and machine learning

Abstract

In the monitoring of a complex electric grid, it is of paramount impor-tance to provide operators with early warnings of anomalies detected on the network,along with a precise classification and diagnosis of the specific fault type. In thispaper, we propose a novel multi-stage early warning system prototype for electricgrid fault detection, classification, subgroup discovery, and visualization. In thefirst stage, a computationally efficient anomaly detection method based on quar-tiles detects the presence of a fault in real time. In the second stage, the fault isclassified into one of nine pre-defined disaster scenarios. The time series data arefirst mapped to highly discriminative features by applying dimensionality reductionbased on temporal autocorrelation. The features are then mapped through one ofthree classification techniques: support vector machine, random forest, and artificialneural network. Finally in the third stage, intra-class clustering based on dynamictime warping is used to characterize the fault with further granularity. Results onthe Bonneville Power Administration electric grid data show that i) the proposedanomaly detector is both fast and accurate; ii) dimensionality reduction leads todramatic improvement in classification accuracy and speed; iii) the random forestmethod offers the most accurate, consistent, and robust fault classification; and iv)time series within a given class naturally separate into five distinct clusters whichcorrespond closely to the geographical distribution of electric grid buses.