Robust Power System State Estimation using Physics-Informed Neural Networks

--Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal and faulty conditions, while also showing potential in addressing security concerns such as data manipulation attacks. Experimental results show that the proposed approach outperforms traditional machine learning models, achieving up to 83% higher accuracy on unseen subsets of the training dataset and 65% better performance on entirely new, unrelated datasets. Experiments also show that during a data manipulation attack against a critical bus in a system, the PINN can be up to 93% more accurate than an equivalent neural network. The escalating global electricity demand, driven by rapid urbanization, transportation electrification, and digital technology proliferation, has underscored the need for robust and stable power systems. Ensuring the stability and security of critical infrastructures, particularly power transmission networks, is essential for economic stability and public safety. However, the growing complexity of modern grids, driven by renewable energy integration, adoption of smart grid technologies, and interconnected networks, presents significant challenges in monitoring, control, and system resilience [1], [2]. In particular, addressing challenges related to real-time data management and stability has become increasingly critical, necessitating advanced monitoring schemes to ensure system stability and integrity.