Physics-Informed Neural Networks for Accelerating Power System State Estimation

TL;DR

This paper introduces a physics-informed neural network approach to accelerate power system state estimation, reducing computation time while maintaining high accuracy, demonstrated on the IEEE 14-bus system.

Contribution

It proposes a novel PINN-based method that incorporates physical laws to improve speed and accuracy of power system state estimation.

Findings

Achieves up to 11% increase in accuracy

Reduces standard deviation of results by 75%

Speeds up convergence by 30%

Abstract

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.