Automated Power System Fault Prediction and Precursor Discovery Using Multi-Modal Data

Electric power system operators monitor large multi-modal data streams from wide service areas. The current data setups stand to get more complex as utilities add more smart-grid sensors to collect additional data from power system substations and other in-situ locations. We propose a methodology to utilize multi-modal data for automated power system fault prediction, and precursor discovery that takes advantage of not only the utility owned measurements but also an abundance of data from other related databases such as weather observation systems. The process is automated to help operators analyze multi-modal data that may be impossible to process manually due to the size and variety. We automatically preprocess multi-source data and learn a joint latent representation from collocated streamed, sparse, and high-dimensional data collected from Phasor Measurement Units and external weather data. Then we utilize multi-instance learning to predict events and discover precursors simultaneously without relying on post-mortem studies of fault signatures. We apply the proposed methodology to provide early predictions of faults in the U.S. Western Interconnection. AU-ROC of 0.94 is achieved in predicting events by utilizing information 5 hours before event time using season-specific models. We show how precursors can be extracted from multi-modal data and interpreted for predicted events.