Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
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| Main Authors: | , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866915787115069440 |
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| author | Moshtagh, Shiva Sifat, Anwarul Islam Azimian, Behrouz Pal, Anamitra |
| author_facet | Moshtagh, Shiva Sifat, Anwarul Islam Azimian, Behrouz Pal, Anamitra |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2212_04592 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes Moshtagh, Shiva Sifat, Anwarul Islam Azimian, Behrouz Pal, Anamitra Systems and Control 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. |
| title | Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2212.04592 |