This paper addresses the challenge of transient stability in power systems with missing parameters and uncertainty propagation in swing equations. We introduce a novel application of Physics-Informed Neural Networks (PINNs), specifically an Ensemble of PINNs (E-PINNs), to estimate critical parameters like rotor angle and inertia coefficient with enhanced accuracy and reduced computational load. E-PINNs capitalize on the underlying physical principles of swing equations to provide a robust solution. Our approach not only facilitates efficient parameter estimation but also quantifies uncertainties, delivering probabilistic insights into the system behavior. The efficacy of E-PINNs is demonstrated through the analysis of 1-bus and 2-bus systems, highlighting the model's ability to handle parameter variability and data scarcity. The study advances the application of machine learning in power system stability, paving the way for reliable and computationally efficient transient stability analysis.
@article{arxiv.2311.12947,
title = {PINNs-Based Uncertainty Quantification for Transient Stability Analysis},
author = {Ren Wang and Ming Zhong and Kaidi Xu and Lola Giráldez Sánchez-Cortés and Ignacio de Cominges Guerra},
journal= {arXiv preprint arXiv:2311.12947},
year = {2023}
}