TY - GEN
T1 - Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
AU - Moshtagh, Shiva
AU - Sifat, Anwarul Islam
AU - Azimian, Behrouz
AU - Pal, Anamitra
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.
AB - 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 systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.
KW - Graph neural network (GNN)
KW - Machine learning (ML)
KW - State estimation (SE)
KW - Topology change
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U2 - 10.1109/NAPS58826.2023.10318579
DO - 10.1109/NAPS58826.2023.10318579
M3 - Conference contribution
AN - SCOPUS:85179549736
T3 - 2023 North American Power Symposium, NAPS 2023
BT - 2023 North American Power Symposium, NAPS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 North American Power Symposium, NAPS 2023
Y2 - 15 October 2023 through 17 October 2023
ER -