Compressive Sensing-Based Topology Identification for Smart Grids

Abstract

Smart grid (SG) technology transforms the traditional power grid from a single-layer physical system to a cyber–physical network that includes a second layer of information. Collecting, transferring, and analyzing the huge amount of data that can be captured from different parameters in the network, together with the uncertainty that is caused by the distributed power generators, challenge the standard methods for security and monitoring in future SGs. Other important issues are the cost and power efficiency of data collection and analysis, which are highlighted in emergency situations such as blackouts. This paper presents an efficient dynamic solution for online SG topology identification (TI) and monitoring by combining concepts from compressive sensing (CS) and graph theory. In particular, the SG is modeled as a huge interconnected graph, and then using a dc power-flow model under the probabilistic optimal power flow (P-OPF), TI is mathematically reformulated as a sparse-recovery problem (SRP). This problem and challenges therein are efficiently solved using modified sparse-recovery algorithms. Network models are generated using the MATPOWER toolbox. Simulation results show that the proposed method represents a promising alternative for real-time monitoring in SGs.