Reweighted Compressed Sensing-Based Smart Grids Topology Reconstruction With Application to Identification of Power Line Outage

Abstract

Smart grid (SG) can automatically collect a large amount of data of different power parameters through different sensors, which has become the future trend of power systems, especially for real-time monitoring needs. However, due to the limitation of detection technology and measurement cost, direct measurement of SG topology is difficult. Thus, reconstructing the topology of SG through measurement data is important yet challenging. In this article, we leverage a graph theory-based power network model and propose to transform the SG topology reconstruction (SGTR) problem into a sparse recovery problem. By exploiting the framework of compressed sensing, the network structure can be reconstructed from a small number of observations. In order to enhance the reconstruction performance, we extract three underlying features of the SG, namely, the symmetry feature, diagonal feature, and cluster feature. Thus, the symmetric reweighting of modified clustered orthogonal matching pursuit method was proposed to integrate these three features simultaneously to improve the performance of SGTR. In order to verify the efficiency of the proposed method, we conduct extensive experiments based on the MATPOWER benchmark toolbox. Compared with some state-of-the-art methods, we find that the reconstruction performance of the proposed method is obviously improved. In addition, based on the reconstructed SG topology, a sparse solution based on QR decomposition was also proposed to locate the power line outages accurately.