2020/11/21 by Shweta Dahale, Dahale, Shweta, Balasubramaniam Natarajan +1 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #Time Series Analysis and Forecasting #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.10738
openalex publication_date 2020/11/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired time-scale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.