Smart grid topology identification using sparse recovery

The technology of smart grid (SG) shapes the traditional power grid into a dynamical network which includes a layer of information that flows through the energy system. Recorded data from a variety of parameters in SGs, can improve analysis of different supervisory problems, but an important issue is the cost and power efficiency in data analysis procedures. This paper presents an efficient solution for topology identification (TI) and monitoring activities in SG. The basic idea of this work comes from combining the sparse recovery theory with graph theory concepts. The power network is modeled as a large interconnected graph, which can be evaluated with the DC power-flow model. Therefore the topology identification for such a system is mathematically reformulated as a sparse recovery problem (SRP), which can be efficiently solved using SRP solvers. In this paper, the network models have been generated with MATPOWER toolbox, and Matlab based simulation results have shown that the proposed method represents a promising approach for real time TI in SGs.