Network Identification Using μ-PMU and Smart Meter Measurements

The network identification plays a very prominent role for the network operator to accomplish the various objectives such as state-estimation, monitoring, control, planning, and real-time analytics. The network structure varies from time-to-time and its details are often not available with the network operator. To address this issue, in this article, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter and microphasor measurement unit (mu) measurements. The presented algorithm is divided into two sections 1) approximate parameter evaluation through regression, to extract the partial topology information and 2) complete network topology identification through the ADMM framework. This algorithm accomplishes the objectives of identifying the network configuration, branch parameters (e.g., conductance and susceptance), and change in branch parameters. Simulation results demonstrate the effectiveness of the presented algorithm on the benchmarked IEEE 13-bus and IEEE 123-bus feeders under various operating scenarios. Furthermore, the presented framework illustrates excellent network identification even with the presence of the stochastic nature of renewable power generation. The presented algorithm exhibits an excellent performance even with the consideration of noise in both measurements. In addition, the comparative performance is carried out on the benchmarked unbalanced IEEE 13-bus and balanced IEEE 33-bus feeders to highlight the efficacy of the presented framework over the state-of-art framework.

Automated summary

In this article, a framework based on ADMM is presented to identify the network topology and line parameters using smart meter and microphasor measurement unit. The algorithm achieves the objectives of identifying network configuration and parameters through approximation and ADMM framework. Simulation results demonstrate the effectiveness of the algorithm under different operating scenarios and its excellent performance in the presence of renewable power generation.