Fault Detection and Localization in Smart Grid: A Probabilistic Dependence Graph Approach

Fault localization in the nation´s power grid networks is known to be challenging, due to the massive scale and inherent complexity. In this study, we model the phasor angles across the buses as a Gaussian Markov random field (GMRF), where the partial correlation coefficients of GMRF are quantified in terms of the physical parameters of power systems. We then take the GMRF-based approach for fault diagnosis, through change detection and localization in the partial correlation matrix of GMRF. Specifically, we take advantage of the topological hierarchy of power systems, and devise a multi-resolution inference algorithm for fault localization, in a distributed manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.