State estimation in electric power systems leveraging graph neural networks
The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state
variables based on the available set of measurements in the power system. Because phasor …
variables based on the available set of measurements in the power system. Because phasor …
Graph neural networks on factor graphs for robust, fast, and scalable linear state estimation with PMUs
As phasor measurement units (PMUs) become more widely used in transmission power
systems, a fast state estimation (SE) algorithm that can take advantage of their high sample …
systems, a fast state estimation (SE) algorithm that can take advantage of their high sample …
Physics-informed graphical neural network for power system state estimation
State estimation is highly critical for accurately observing the dynamic behavior of the power
grids and minimizing risks from cyber threats. However, existing state estimation methods …
grids and minimizing risks from cyber threats. However, existing state estimation methods …
Exploration of graph computing in power system state estimation
With the increased complexity of power systems due to the integration of smart grid
technologies and renewable energy resources, more frequent changes have been …
technologies and renewable energy resources, more frequent changes have been …
State estimation in smart grids using temporal graph convolution networks
MJ Hossain… - 2021 North American …, 2021 - ieeexplore.ieee.org
State estimation (SE) is one of the key functions of smart grids. The availability of large
volumes of measurement data introduces new opportunities for improving and …
volumes of measurement data introduces new opportunities for improving and …
Physics-aware neural networks for distribution system state estimation
AS Zamzam, ND Sidiropoulos - IEEE Transactions on Power …, 2020 - ieeexplore.ieee.org
The distribution system state estimation problem seeks to determine the network state from
available measurements. Widely used Gauss-Newton approaches are very sensitive to the …
available measurements. Widely used Gauss-Newton approaches are very sensitive to the …
Physics-informed graphical neural network for parameter & state estimations in power systems
L Pagnier, M Chertkov - arXiv preprint arXiv:2102.06349, 2021 - arxiv.org
Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the
system engineering. They need to be done automatically, fast and frequently, as …
system engineering. They need to be done automatically, fast and frequently, as …
Parameter identification in power transmission systems based on graph convolution network
Z Wang, M Xia, M Lu, L Pan, J Liu - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Parameter Identification plays an important role in electric power transmission systems.
Existing approaches for parameter identification tasks typically have two limitations:(1) They …
Existing approaches for parameter identification tasks typically have two limitations:(1) They …
[图书][B] Enhancement of distribution system state estimation using pruned physics-aware neural networks
Realizing complete observability in the three-phase distribution system remains a challenge
that hinders the implementation of classic state estimation algorithms. In this paper, a new …
that hinders the implementation of classic state estimation algorithms. In this paper, a new …
Real-time power system state estimation and forecasting via deep unrolled neural networks
Contemporary power grids are being challenged by rapid and sizeable voltage fluctuations
that are caused by large-scale deployment of renewable generators, electric vehicles, and …
that are caused by large-scale deployment of renewable generators, electric vehicles, and …