A hierarchical framework for smart grid anomaly detection using large-scale smart meter data

Real-time monitoring and control of smart grids (SGs) is critical to the enhancement of reliability and operational efficiency of power utilities. We develop a real-time anomaly detection framework, which can be built based upon smart meter (SM) data collected at the consumers' premises. The model is designed to detect the occurrence of anomalous events and abnormal conditions at both lateral and customer levels. We propose a generative model for anomaly detection that takes into account the hierarchical structure of the network and the data collected from SMs. We also address three challenges existing in SG analytics: 1) large-scale multivariate count measurements; 2) missing points; and 3) variable selection. We present the effectiveness of our approach with numerical experiments.