PROJECT TITLE :

Compressive Sensing-Based Topology Identification for Smart Grids

ABSTRACT:

Good grid (SG) technology transforms the ancient power grid from one-layer physical system to a cyber-physical network that features a second layer of information. Collecting, transferring, and analyzing the massive amount of data that may be captured from completely different parameters in the network, along with the uncertainty that's caused by the distributed power generators, challenge the standard ways for security and monitoring in future SGs. Other vital problems are the price and power efficiency of data collection and analysis, that are highlighted in emergency things such as blackouts. This paper presents an economical dynamic answer for on-line SG topology identification (TI) and monitoring by combining ideas from compressive sensing (CS) and graph theory. In specific, the SG is modeled as a large interconnected graph, and then employing a dc power-flow model under the probabilistic optimal power flow (P-OPF), TI is mathematically reformulated as a sparse-recovery drawback (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 technique represents a promising various for real-time monitoring in SGs.