Smart-grid topology identification using sparse recovery

Smart grid (SG) technology reshapes the traditional power grid into a dynamical network with a layer of information that flows along the energy system. Recorded data from a variety of parameters in SGs can improve the analysis of different supervisory problems, but an important issue is their cost and power efficiency in data analysis procedures. This paper develops an efficient solution for power network topology identification and monitoring activities in SG. The basic idea combines optimization-based sparse-recovery techniques with a graph theory foundation. The power network (PN) is modeled as a large interconnected graph, which can be evaluated with the dc power-flow model. It has been shown that topology identification for such a system can mathematically be reformulated as a sparse-recovery problem (SRP), and the corresponding SRP can efficiently be solved using SRP solvers. In this study, we especially exploit the concentration of nonzero elements in the corresponding sparse vectors around the main diagonal in the nodal admittance or structure matrix of the PN to improve the results. The network models have been generated with the MATPOWER toolbox, and MATLAB-based simulation results have indicated the promising performance of the proposed method for real-time topology identification (TI) in SGs.