A Fast Optimal Power Flow Algorithm Using Powerball Method

Zhang, Haitao; Sun, Weigao; Li, Yuanzheng; Fu, Dongfei; Yuan, Ye

doi:10.1109/tii.2019.2909328

Cited by 13 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is assumed that the smart meters are installed at certain buses, while the phasor measurement units are installed at the rest of the buses. Assuming that 'Gij' and 'Bij' are the susceptance and conductance of a line between the nodes 'i' and 'j' (i, j ∈ ℕ), then the active and reactive power injections at a given bus can be written as [23],…”

Section: A Available Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

Network Identification Using μ-PMU and Smart Meter Measurements

Shah

Zhao

2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

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, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter (SM) and micro phasor measurement unit (µ-PMU) 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.

show abstract

Section: A Available Measurementsmentioning

confidence: 99%

“…A pseudo-inverse technique [23], is then used to compute the approximate values of 'Gij # ' matrix as,…”

Section: ) Step-i: Parameter Evaluation Through Regressionmentioning

confidence: 99%