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This content will become publicly available on October 15, 2024

Title: 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 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.  more » « less
Award ID(s):
2145063
NSF-PAR ID:
10486850
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Page Range / eLocation ID:
1 to 6
Format(s):
Medium: X
Location:
Asheville, NC, USA
Sponsoring Org:
National Science Foundation
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