# Physics-Informed Neural Networks for Accelerating Power System State Estimation

arXiv: https://arxiv.org/abs/2310.03088 · HTML: https://mlsanity.com/paper/2310.03088/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Real-time Power System State Estimation and Forecasting via Deep Neural Networks](https://mlsanity.com/paper/1811.06146/) (2018)
- [Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems](https://mlsanity.com/paper/2102.06349/) (2021)
- [Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning](https://mlsanity.com/paper/1811.02756/) (2018)
- [Physics-Aware Neural Networks for Distribution System State Estimation](https://mlsanity.com/paper/1903.09669/) (2019)

## Built on by (most cited first, 5 of 5)

- [GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis](https://mlsanity.com/paper/2608.09921/) (2026)
- [Robust Power System State Estimation using Physics-Informed Neural Networks](https://mlsanity.com/paper/2507.05874/) (2025)
- [Adaptive Informed Deep Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2412.02659/) (2024)
- [PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems](https://mlsanity.com/paper/2507.06712/) (2025)
- [Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks](https://mlsanity.com/paper/2604.22784/) (2026)

## Abstract (arXiv)

State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can be computationally intensive, particularly for large-scale power systems. In this paper, a novel approach that leverages the inherent physical knowledge of power systems through the integration of PINNs is proposed. By incorporating physical laws as prior knowledge, the proposed method significantly reduces the computational complexity associated with state estimation while maintaining high accuracy. The proposed method achieves up to 11% increase in accuracy, 75% reduction in standard deviation of results, and 30% faster convergence, as demonstrated by comprehensive experiments on the IEEE 14-bus system.