Robust Power System State Estimation using Physics-Informed Neural Networks

TL;DR

This paper introduces a physics-informed neural network approach to improve the accuracy and robustness of power system state estimation, especially under faulty or cyber-attack conditions.

Contribution

It embeds physical laws into neural networks to enhance power system state estimation accuracy and robustness against faults and cyber threats.

Findings

Achieves up to 83% higher accuracy on unseen data

Performs 65% better on unrelated datasets

Up to 93% more accurate during data manipulation attacks

Abstract

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.