Distributed nonlinear state estimation in electric power systems using graph neural networks
Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on
all types of measurements available in the power system, is usually solved using the …
all types of measurements available in the power system, is usually solved using the …
[PDF][PDF] Distributed power system state estimation using graph convolutional neural networks
State estimation plays a key role in guaranteeing the safe and reliable operation of power
systems. This is a complex problem due to the noisy and unreliable nature of the …
systems. This is a complex problem due to the noisy and unreliable nature of the …
State Estimation for Power Distribution System Using Graph Neural Networks
State estimation is critical to maintaining system stability and reliability as it enables real-
time monitoring of the power system operation and facilitates fault detection, minimizing the …
time monitoring of the power system operation and facilitates fault detection, minimizing the …
Graph convolutional networks for power system state estimation
Q Yang, A Sadeghi - Proceedings of IEEE Smartgridcom Conference, 2020 - par.nsf.gov
Power system state estimation (PSSE) aims at finding the voltage magnitudes and angles at
all generation and load buses, using meter readings and other available information. PSSE …
all generation and load buses, using meter readings and other available information. PSSE …
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 …
Power system state estimation using gauss-newton unrolled neural networks with trainable priors
Power system state estimation (PSSE) aims at finding the voltage magnitudes and angles at
all generation and load buses, using meter readings and other available information. PSSE …
all generation and load buses, using meter readings and other available information. PSSE …
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 …
Application of physics-based graph convolutional network in real-time state estimation of under-determined distribution grids
S Stock, M Dressel, D Babazadeh… - 2022 IEEE PES …, 2022 - ieeexplore.ieee.org
Emerging trends in distribution grids such as sector integration and high penetration of
distributed energy resources increase uncertainty and volatility of the power system. Under …
distributed energy resources increase uncertainty and volatility of the power system. Under …
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 …
Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
Recently, there has been a major emphasis on developing data-driven approaches
involving machine learning (ML) for high-speed static state estimation (SE) in power …
involving machine learning (ML) for high-speed static state estimation (SE) in power …