2022/12/08 by Shiva Moshtagh, Moshtagh, Shiva, Anwarul Islam Sifat +5 · 1 citation
Engineering · #FOS: Electrical engineering #Power System Optimization and Stability #Power Systems Fault Detection #Smart Grid and Power Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.04592
openalex publication_date 2022/12/08 · openalex created_date 2022/12/25 · openalex updated_date 2026/07/28
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.