Smart-Grid Topology Identification Using Sparse Recovery

Abstract

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 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 (PNTI) 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 work 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 TI in SGs.

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

“…Generally, the dynamics of CDNs are determined by the topology structure and dynamical properties of nodes, which indicates that the dynamical behavior of CDNs is difficult to understand when the communication topology is unknown [5][6][7]. Hence, it is significant to identify the topology of CDNs, see [8][9][10][11][12][13][14][15][16][17][18][19] and references therein.…”