# PINNs-Based Uncertainty Quantification for Transient Stability Analysis

arXiv: https://arxiv.org/abs/2311.12947 · HTML: https://mlsanity.com/paper/2311.12947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Physics-Informed Neural Networks for Power Systems](https://mlsanity.com/paper/1911.03737/) (2019)
- [Physics-Informed Neural Networks for AC Optimal Power Flow](https://mlsanity.com/paper/2110.02672/) (2021)
- [Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization](https://mlsanity.com/paper/2103.17004/) (2021)
- [Transient Stability Analysis with Physics-Informed Neural Networks](https://mlsanity.com/paper/2106.13638/) (2021)
- [Nonintrusive Uncertainty Quantification of Dynamic Power Systems Subject to Stochastic Excitations](https://mlsanity.com/paper/2001.06848/) (2020)
- [Physics-Constrained Backdoor Attacks on Power System Fault Localization](https://mlsanity.com/paper/2211.04445/) (2022)

## Abstract (arXiv)

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.