A Reweighted l(1)-Minimization Algorithm for Joint Topology and Line Parameter Identification in Electric Grids

Topology and line parameter information is essential to the operation and control of electric grids. However, the gird topology may frequently change with the wide adoption of distributed energy resources, and the line parameters may be missing or inaccurate. The widespread deployment of advanced metering infrastructure enables the line parameters estimation and topology identification from a data-driven perspective. In this paper, we propose a reweighted l(1)-minimization algorithm to deal with the joint topology and line parameter estimation problem by leveraging the sparsity of topology structure. A modified alternating descent sub-optimal method is proposed to estimate the admittance matrix of the power system using nodal voltage and current phasor measurements. Extensive simulations performed on IEEE 14 and 57-bus benchmark systems are presented to substantiate the effectiveness of the proposed algorithm.